Cancer Research Communications

Triple negative breast cancer (TNBC) is a highly heterogeneous set of diseases that has, until recently, lacked any FDA-approved, molecularly targeted therapeutics. Thus, systemic chemotherapy regimens remain the standard of care for many. Unfortunately, even combination chemotherapy is ineffective for many TNBC patients, and side-effects can be severe or lethal. Identification of predictive biomarkers for chemotherapy response would allow for the prospective selection of responsive patients, thereby maximizing efficacy and minimizing unwanted toxicities. Here, we leverage a cohort of TNBC PDX models with responses to single-agent docetaxel or carboplatin to identify biomarkers predictive for differential response to these two drugs. To demonstrate their ability to function as a preclinical cohort, PDX were molecularly characterized using whole-exome DNA sequencing, RNAseq transcriptomics, and mass spectrometry-based total proteomics to show proteogenomic consistency with TCGA and CPTAC clinical samples. Focusing first on the transcriptome, we describe a network-based computational approach to identify candidate epithelial and stromal biomarkers of response to carboplatin (MSI1, TMSB15A, ARHGDIB, GGT1, SV2A, SEC14L2, SERPINI1, ADAMTS20, DGKQ) and docetaxel (ITGA7, MAGED4, CERS1, ST8SIA2, KIF24, PARPBP). Biomarker panels are predictive in PDX expression datasets (RNAseq and Affymetrix) for both taxane (docetaxel or paclitaxel) and platinum-based (carboplatin or cisplatin) response, thereby demonstrating both cross expression platform and cross drug class robustness. Biomarker panels were also predictive in clinical datasets with response to cisplatin or paclitaxel, thus demonstrating translational potential of PDX-based preclinical trials. This network-based approach is highly adaptable and can be used to evaluate biomarkers of response to other agents.

Although immune checkpoint inhibitors targeting the programmed death-ligand 1 (PD-L1) axis have transformed the treatment of recurrent and metastatic head and neck squamous cell carcinoma (HNSCC), durable clinical responses remain limited to a minority of patients, and the determinants of treatment resistance remain incompletely understood. Human papillomavirus (HPV) infection, alcohol consumption, tobacco use, and are the three most prominent etiological risk factors for HNSCC; however, despite their well-established individual roles in disease development, the influence of their combined exposure on PD-L1 axis regulation and immunotherapy response remains largely unexplored. In this study, we analyzed multi-omic data from 498 primary HNSCC tumors in The Cancer Genome Atlas (TCGA), stratifying patients into seven subgroups reflecting all observed exposure combinations, with HPV status determined directly from RNA-sequencing reads using Pathoscope. Notably, PD-L1 (CD274) expression was significantly downregulated in the triple-exposure cohort (1.51-fold reduction, p < 0.05), along with reduced expression of the upstream regulator JAK2 (1.44-fold reduction, p < 0.05) being seen. Immune deconvolution suggested progressively greater immune infiltration with accumulating exposures, yet gene set enrichment analysis revealed concurrent downregulation of T cell activation, T cell differentiation, and NK cell-mediated immunity in the triple-exposure subgroup -- consistent with an inflamed but functionally suppressed tumor microenvironment. Preliminary integration with an independent single-cell RNA-sequencing dataset of HNSCC patients undergoing neoadjuvant PD-1/CTLA-4 blockade further suggested enrichment of granulocyte and regulatory T cell populations among non-responding patients. Survival differences between cohorts were also observed, likely reflecting biological heterogeneity driven by distinct etiologies and differences in clinical presentation across exposure groups. Together, these findings provide early insights into how multi-etiological exposure burden may shape PD-L1 axis dysregulation and immune microenvironment remodeling in HNSCC, with potential implications for patient stratification in checkpoint inhibitor therapy.

High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative "extinction therapy" strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular "magic bullet" but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An "IMOme" score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.

Patient-derived organoids from breast cancer brain metastases enable real-time drug sensitivity testing integrated with genomic profiling. Drug response varied by subtype and molecular alterations. PI3K inhibitors showed activity regardless of PIK3CA mutation status. Pronounced tumor heterogeneity highlighted the urgent need for effective therapies personalized for each patient. Functional assays and molecular matching can help tailor therapy for patients who need the most effective next treatment quickly and warrant further translational evaluation to address this unmet need.

Malignant peripheral nerve sheath tumors (MPNST) are aggressive soft tissue sarcomas that often develop in patients with neurofibromatosis type 1 (NF1-MPNST), but can occur sporadically. Through a multi-institution collaboration, we have developed 13 NF1-associated MPNST patient-derived xenografts (PDX). Genomic analysis of the PDX-tumor pairs identified somatic mutations in NF1 (61%), SUZ12 (61%), EED (15%), and TP53 (15%), and chromosome 8 (Chr8) gain (77%), consistent with published data. Pre-clinical models that capture this molecular heterogeneity are needed to identify and prioritize effective drug candidates for clinical translation. Here, we describe the successful development of a medium-throughput ex vivo 3D microtissue model with several advantages over 2D cell line growth, which can be utilized to predict drug response in vivo. Herein, we present proof-of-principle of this PDX-to-microtissue system, using four genomically representative MPNST and three drugs. This work highlights the development of a novel ex vivo to in vivo preclinical platform in MPNST that successfully captures the genomic diversity observed in patients and represents a resource to identify future therapeutic strategies.

Breast cancer affects 1/8 of women throughout their lifetimes, with over 90% of cancer deaths being caused by metastasis. However, metastasis poses unique challenges to research, as complex changes in the microenvironment in different metastatic sites and difficulty obtaining tissue for study hinder the ability to examine in depth the changes that occur during metastasis. Rapid autopsy programs thus fill a unique need in advancing metastasis research. Here, we describe our protocol and processes for establishing and improving the US-based Hope for OTHERS (Our Tissue Helping Enhance Research and Science) program for organ donation in metastatic breast cancer. As of August 2024, we consented 114 patients and performed 37 autopsies, from which we collected 551 unique metastatic frozen tumor samples, 1244 FFPE blocks, 90 longitudinal liquid biopsy samples and developed 14 patient-derived organoid and 8 patient-derived xenograft models. We report in-depth clinical and histopathological information and discuss extensive new research and novel findings in patient outcomes, metastatic phylogeny, and factors in successful living model development. Our results reveal key logistical and protocol improvements that are uniquely beneficial to certain programs based on identifiable features, such as working closely with patient advocates, methods to rescue RNA quality in cases where tissue quality may degrade due to time delays, as well as guidelines and future expansions of our program. Statement of SignificanceRapid autopsy programs are unique research settings with huge potential for studying metastatic cancer, however, they have complex research challenges. Our work provides a valuable resource in advancing this field of research.

Ewing sarcoma, an oncofusion-driven primary bone tumor, can occur in the setting of various germline mutations in DNA damage repair pathway genes. We recently reported our discovery of a germline mutation in the DNA damage repair protein BARD1 (BRCA1-associated RING domain-1) in a patient with Ewing sarcoma. BARD1 is recruited to the site of DNA double stranded breaks via the poly(ADP-ribose) polymerase (PARP) protein and plays a critical role in DNA damage response pathways including homologous recombination. PARP inhibitors (PARPi) are effective against Ewing sarcoma cells in vitro, though have demonstrated limited success in clinical trials to date. In order to assess the impact of BARD1 loss on Ewing sarcoma sensitivity to PARP inhibitor therapy, we generated the novel PSaRC318 patient-derived Ewing tumor cell from our patient with a germline BARD1 mutation and then analyzed the response of these cells to PARPi. We demonstrate that PSaRC318 cells are sensitive to PARP inhibition and by testing the effect of BARD1 depletion in additional Ewing sarcoma cell lines, we confirm that loss of BARD1 enhances PARPi sensitivity. In certain malignancies, DNA damage can activate the IRF1 (interferon response factor 1) immunoregulatory pathway, and the activation of this pathway can drive immunosuppression through upregulation of the immune checkpoint protein PD-L1. In order to determine the ability of PARPi to alter Ewing tumor immunoregulation, we evaluated whether PARPi results in upregulation of the IRF1-PDL1 pathway. Indeed, we now demonstrate that PARPi leads to increased PD-L1 expression in Ewing sarcoma. Together, these data thus far suggest that while Ewing tumors harboring germline mutations in DNA damage repair proteins may in respond to PARPi in vitro, in vivo benefit of PARPi may only be demonstrated when counteracting the immunosuppressive effects of DNA damage by concurrently targeting immune checkpoint proteins.

For women with advanced ovarian cancer (OC), remission is typically achieved through surgery and combination chemotherapy, with duration largely dependent on tumor sensitivity to platinum-based drugs. Here, we show that tumor-associated glycosaminoglycans (GAGs) influence platinum drug efficacy in preclinical models of ovarian cancer. Due to the complexity of GAG biosynthesis and the involvement of multiple enzymes, traditional transcriptomic and proteomic approaches cannot accurately estimate their levels or correlation with patient response and survival. To address this, we quantitatively analyzed the full compositional profile of GAGs in OC patient-derived xenograft (PDX) models with known carboplatin sensitivity. Our results revealed a significant correlation between carboplatin resistance and high levels of the predominant GAG sequence, chondroitin-4-sulfate (C4S). Further investigation in cellular models demonstrated that high GAG expression reduces carboplatin uptake, DNA adduct formation, and tumor accumulation, whereas the opposite effect was observed for Triplatin, a GAG-targeting platinum agent. These trends were further validated in vivo, where treatment of OC PDX models with varying C4S levels confirmed that carboplatin efficacy decreases while Triplatin activity increases in tumors with high C4S expression. Based on these findings, we established a C4S cut-off score to predict tumor sensitivity, identifying a threshold above which tumors are likely to be carboplatin-resistant but Triplatin-sensitive. Analysis of patient tissue microarrays estimated that 40-83% of OC tumors, depending on subtype, exhibit high C4S expression. Collectively, these findings highlight the predictive power of C4S as a biomarker for platinum response and support the clinical evaluation of Triplatin as a targeted treatment for patients with carboplatin-resistant tumors expressing high levels of C4S. One Sentence SummaryElevated chondroitin-4-sulfate in ovarian cancer results in resistance to carboplatin and sensitivity to Triplatin.

Tumor-derived lactate is increasingly recognized as an immunosuppressive metabolite within the tumor microenvironment (TME), with emerging evidence highlighting its role beyond metabolism to include epigenetic and immune regulatory functions. While prior studies have primarily focused on individual immune checkpoints, most prominently PD-L1, it remains unclear whether lactate broadly coordinates the expression of multiple immune regulatory pathways across distinct tumor types, particularly in the context of chronic exposure mimicking glycolytic tumors. Here, we investigated the relationship between lactate-producing metabolism and immune checkpoint gene expression in four human cancer cell lines representing breast and lung cancer: MCF7 (estrogen receptor-positive breast), MDA-MB-231 (triple-negative breast), A549 (non-small cell lung), and H82 (small cell lung). By manipulating glucose availability and exposure duration to model acute (6 h) versus chronic (48 h) lactate production, and by pharmacologically inhibiting lactate dehydrogenase (LDH) with oxamate, we quantified extracellular lactate accumulation and assessed transcriptional responses of a panel of immune checkpoints (PD-L1, CD80, CD73, LGALS9, VISTA, PVR, CD47, FGL1, STING) and lactate-associated genes (MCT1, MCT4, LDHA, HCAR1) via qPCR. Chronic high-glucose conditions produced robust, LDH-dependent lactate accumulation and were associated with coordinated, lineage-specific remodeling of multiple checkpoint transcripts, whereas acute exposure induced minimal changes. MDA-MB-231 and A549 cells displayed striking but distinct checkpoint patterns under chronic lactate-producing conditions: MDA-MB-231 cells showed strong co-induction of PD-L1 and CD80, while A549 cells exhibited dominant CD80 induction with modest PD-L1 upregulation. H82 cells upregulated PD-L1 alongside CD73, LGALS9, CD47, and CD80, whereas MCF7 cells demonstrated more modest yet coordinated increases across several checkpoints. Chronic glucose exposure resulted in sustained, LDH-dependent lactate accumulation and coordinated induction of multiple immune checkpoint genes, with distinct lineage-specific patterns, e.g., robust PD-L1/CD80 upregulation in MDA-MB-231 versus CD80 dominance in A549. Unsupervised clustering and principal component analysis revealed that duration of glucose exposure, rather than acute glucose availability, was the primary axis of variation and that MCT4 and HCAR1 clustered with strongly induced checkpoints, consistent with a transcriptional program linking lactate export and sensing to immune regulation. These findings support a model in which lactate acts as an upstream regulator of a broader immune escape program, potentially via mechanisms like lactylation and HCAR1 signaling. This work highlights the limitations of single-checkpoint blockade strategies in solid tumors and underscores the potential of targeting lactate metabolism to enhance immunotherapy efficacy in breast and lung cancers.

Binary classification using gene expression data is commonly used to stratify cancers into molecular subgroups that may have distinct prognoses and therapeutic options. A limitation of many such methods is the requirement for comparable training and testing data sets. Here, we describe and demonstrate a self-training implementation of probability ratio-based classification prediction score (PRPS-ST) that facilitates the porting of existing classification models to other gene expression data sets. We demonstrate its robustness through application to two binary classification problems in diffuse large B-cell lymphoma using a diverse variety of gene expression data types and normalization methods.

Diffuse large B-cell lymphoma (DLBCL) is a common, aggressive cancer of notorious genotypic and phenotypic heterogeneity. A major challenge is predicting response to drug treatment, which has typically been done using genomic tools alone with little success. A novel method that incorporates phenotypic profiling for predicting the effectiveness of therapy for individual patients is desperately needed. BioDynamic Imaging (BDI) is a technique for measuring time-dependent fluctuations in back-scattered light through living tumor tissues to identify critical changes in intracellular dynamics that are associated with phenotypic response to drugs. In this study, BDI and RNA sequencing (RNA-seq) data were collected on tumor samples from dogs with naturally occurring DLBCL, an animal model of increasingly recognized relevance to the human disease. BDI and RNA-seq data were combined to identify correlations between gene co-expression modules and linear combinations of biomarkers to provide biological mechanistic interpretations of BDI biomarkers. Using regularized multivariate logistic regression, we combined RNA-seq and BDI data to develop a novel model to accurately classify the clinical response of canine DLBCL to combination chemotherapy (i.e. CHOP). Our model incorporates data on the expression of 4 genes and 3 BDI-derived phenotypic biomarkers, capturing changes in transcription, microtubule related processes, and apoptosis. This pilot study suggests that the combination of multi-scale transcriptomic and phenotypic data can identify patients that respond to a given treatment a priori in a disease that has been difficult to treat. Our work provides an important framework for future development of strategies and treatments in precision cancer medicine.

Poly (ADP-ribose) polymerase inhibitors (PARPi) are standard of care for many BRCA1 deficient cancers, though few cures are achieved. We sought to determine if targeting the protection of the single-strand DNA gaps induced by PARPi in BRCA-deficient cancers could increase efficacy. Replication protein A (RPA) participates in critical protein-protein and protein-DNA interactions to protect single-stranded DNA (ssDNA) and support DNA metabolism. We have reported the optimization of small molecule RPA inhibitors (RPAi) that target protein-ssDNA interactions to chemically exhaust RPA and elicit single-agent anticancer activity. RPAi sensitizes cells to PARPi in BRCA1-deficient non-cancerous cells, where ssDNA gap formation drives therapeutic efficacy. We show that RPAi treatment abolishes PARPi-induced replication gap protection, resulting in genomic instability via replication fork degradation and chromosomal integrity, and that, in vivo, the RPA and PARP targeted combination abrogates cancer growth in a BRCA1-mutant breast cancer model. We find that genetic predispositions to ssDNA gap accumulation correlate with RPAi sensitivity, and BRCA1-proficient cells remain sensitive to combination treatment but require more PARP inhibition to increase ssDNA gaps. RPAi-PARPi combination activity in patient-derived ovarian cancer models demonstrates the utility of targeting gap protection to increase PARPi sensitivity and circumvent resistance. Collectively, this work provides a unifying mechanism of chemical RPA exhaustion as a cancer therapeutic strategy. One Sentence SummaryInhibition of single-strand DNA gap protection potentiates PARP targeted treatment of BRCA1 deficient cancers.

The tumor immune microenvironment (TIME) of breast cancer is a known source of tumor heterogeneity and it has been increasingly recognized as having a role in the course of disease. In the present study, we used a computational approach to dissect the landscape of TIME states among TCGA breast cancer patients. Our central hypothesis is that the pre-existing TIME states represent a dimension which is informative about the prognosis and the response to immunotherapy. In order to test this hypothesis, we first classified breast cancer patients according to their primary TIME status. Next, we describe a TIME-based classification with prognostic value for overall survival among the TCGA patients. We further demonstrated that absolute quantification of mast cells, M0 macrophages, CD8 T cells and neutrophils were predictive of overall survival. In order to identify the TIME states which, predict response to immune checkpoint blockade, we performed a similar analysis of 11 different mouse models of primary invasive breast carcinoma that were subsequently treated with immune checkpoint inhibitor (ICI) therapy. These analyses revealed that the TIME content of M1 macrophages, monocytes and resting dendritic cells were predictive of sensitivity to ICI therapy. Taken together, these results indicate that (1) the landscape of human primary TIME states is diverse and can identify patients with more or less aggressive disease and (2) that pre-existing TIME states may be able to identify patients, of all molecular subtypes of breast cancer, who are good candidates for ICI therapy.View Full Text

BackgroundChromosome 8q (chr8q) copy number gain is associated with high-grade transformation in malignant peripheral nerve sheath tumors (MPNST), an aggressive soft tissue tumor with poor outcomes in the high-risk and metastatic settings. Although chr8q gain is associated with inferior overall survival in patients with MPNST, standard of care therapies do not currently consider stratification by genomic features, including chr8q status. MethodsWe employed a proteogenomic approach to characterize proteomic and transcriptional programs associated with chr8q and nominate drug targets for potential treatment stratification based on chr8q status. We leveraged our growing library of fully characterized MPNST patient-derived xenografts (PDX) and collected LC-MS/MS global and phospho-proteomics measurements for six of these samples. We then integrated these data with transcriptomics and copy number data to identify molecular changes that are correlated with chr8q copy number. We nominated pathways, transcription factors, and kinases that were differentially active in chr8q gain samples and posited that these samples would respond differently to drugs compared to chr8q wildtype samples. We then tested this hypothesis in vitro. ResultsOur results suggest that the chr8q gene MYC may be a key driver of downstream effects that can be targetable with inhibitors of PLK1. Conversely, EGFR inhibition may be more effective in MYC-diploid MPNSTs than those with MYC gain. These results nominate candidate pathways and drug classes to target tumor heterogeneity in MPNST through the proteogenomic integration and drug sensitivity prediction in distinct tumor subpopulations. ConclusionsWe show that integration of multiomics data can identify specific drug therapies to selectively target tumor cells based on chr8q copy number. This not only provides novel avenues for drug nomination going forward but also may be important for stratifying treatment and mitigating resistance in heterogeneous tumors.

The clinical heterogeneity of cancer poses a major challenge for precision medicine. Limited cohort sizes across evolving assay platforms impede reliable biomarker discovery. Here, we systematically evaluate how to integrate data from four transcriptomics platforms: bulk and single-cell (sc) RNA sequencing (RNA-seq), NanoString, and microarray for predictive modeling in cancer. We use high-grade serous carcinoma (HGSC) of tube-ovarian origin as a model system, as it is highly heterogeneous in both biology and assay data. We find that using fold-change of gene expression in patients with matched pre- and post-neoadjuvant chemotherapy samples reduces inter-patient and inter-assay variability but is insufficient to overcome platform-specific biases. Microarray and scRNA-seq data exhibit systematic biases, while RNA-seq and NanoString show the most promise for combination into a single training cohort. To mitigate inter-assay limitations, we generate a new data set of HGSC tumor samples profiled with both RNA-seq and NanoString, and use it to identify the limits of detection and optimal harmonization strategies. Our approaches enable integration of cohorts for separate and combined RNA-seq and NanoString predictive models of disease recurrence (test-set AUROCs > 0.8), validated in external microarray cohorts. We leverage single-cell and bulk RNA-seq network-based analyses to provide mechanistic context for genes in the predictive models. Our models indicate that GBP4 expression is a key predictor of recurrence and marks immune remodeling towards cytotoxicity. We provide an interactive web portal to facilitate exploration of data and results. These findings guide cross-assay harmonization of transcriptomic data and enable improved predictive modeling in heterogeneous cancers. Statement of SignificanceWe present a framework for integrating RNA-seq, NanoString, microarray, and single-cell transcriptomic data for predictive modeling, enabling robust biomarker discovery in heterogeneous cancers and identifying GBP4 as a marker of immune remodeling.

Breast cancer is categorized by the molecular and histologic presentation of the tumor, with the major histologic subtypes being No Special Type (NST) and Invasive Lobular Carcinoma (ILC). ILC are characterized by growth in a single file discohesive manner with stromal infiltration attributed to their hallmark pathognomonic loss of E-cadherin (CDH1). Few ILC cell line models are available to researchers. Here we report the successful establishment and characterization of a novel ILC cell line, WCRC-25, from a metastatic pleural effusion from a postmenopausal Caucasian woman with metastatic ILC. WCRC-25 is an ER-negative luminal epithelial ILC cell line with both luminal and Her2-like features. It exhibits anchorage independent growth and haptotactic migration towards Collagen I. Sequencing revealed a CDH1 Q706* truncating mutation, together with mutations in FOXA1, CTCF, BRCA2 and TP53, which were also seen in a series of metastatic lesions from the patient. Copy number analyses revealed amplification and deletion of genes frequently altered in ILC while optical genome mapping revealed novel structural rearrangements. RNA-seq analysis comparing the primary tumor, metastases and the cell line revealed signatures for cell cycle progression and receptor tyrosine kinase signaling. To assess targetability, we treated WCRC-25 with AZD5363 and Alpelisib confirming WCRC-25 as susceptible to PI3K/AKT signaling inhibition as predicted by our RNA sequencing analysis. In conclusion, we report WCRC-25 as a novel ILC cell line with promise as a valuable research tool to advance our understanding of ILC and its therapeutic vulnerabilities. Financial supportThe work was in part supported by a Susan G Komen Leadership Grant to SO (SAC160073) and NCI R01 CA252378 (SO/AVL). AVL and SO are Komen Scholars, Hillman Foundation Fellows and supported by BCRF. This project used the UPMC Hillman Cancer Center and Tissue and Research Pathology/Pitt Biospecimen Core shared resource which is supported in part by award P30CA047904. This research was also supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483. Finally, partial support was provided by the Magee-Womens Research Institute and Foundation, The Shear Family Foundation, and The Metastatic Breast Cancer Network.

HER2 (ERBB2) is a major therapeutic drug target in breast cancer and The Cancer Genome Atlas (TCGA) Breast Cancer project and other studies have identified HER2 activating mutations in breast cancers without HER2 gene amplification. HER2 activating mutations occur in 2-5% of metastatic breast cancer patients (MBC), and clinical trials have shown that the irreversible pan-HER tyrosine kinase inhibitor, neratinib, produces a 31-40% clinical benefit rate for HER2 mutated MBC patients. We developed breast cancer patient-derived xenografts (PDX) from ER+, HER2 mutated MBC patients and used them to test neratinib-based drug combinations. Using organoid culture of these PDX breast cancer cells, we performed rapid, high-throughput ex vivo screening assays to test novel drug combinations. These organoid culture experiments identified drug synergy with the neratinib plus ado-trastuzumab emtansine (T-DM1) and neratinib plus vinorelbine combinations and we validated these results with in vivo PDX experiments. Statement of SignificancePDXs are a ready source of human cancer organoids, and with thousands of PDXs already available worldwide, PDX derived organoids (PDxOs) can dramatically accelerate cancer drug testing. This strategy of PDxO drug testing is particularly useful for rare cancer subtypes or mutations to identify the most promising treatment strategies for clinical trials testing.

Extrachromosomal DNA (ecDNA) is a mechanism of oncogene amplification that occurs in many cancers and has been linked to worse patient outcomes. While ecDNA has previously been identified in cutaneous melanoma, it has yet to be described in cutaneous squamous cell carcinoma (cSCC). Using whole genome sequencing and transcriptomic data of primary and metastatic cSCC tumors, we establish the presence of ecDNA in cSCC and confirm elevated expression of ecDNA-associated genes. We also find that the presence of ecDNA correlates with a higher lymph node ratio, a measure of disease severity. Together, these findings are the first to report ecDNA in cSCC and suggest that ecDNA gene amplifications promote an aggressive cSCC phenotype.

Patients with triple-negative breast cancer (TNBC) experience high recurrence rates despite current interventions, which includes radiation therapy (RT). Tumor cells thought to be involved in recurrence survive in part due to their interactions with irradiated fibroblasts following treatment. How fibroblasts metabolically respond to RT and influence the behavior of TNBC cells is poorly understood. In this study, we demonstrate that irradiated fibroblasts undergo a mitochondrial stress response that is regulated by autophagy, resulting in a metabolic profile characterized by high levels of mitochondrial respiration and fatty acid oxidation. This stress response in fibroblasts induces an aggressive phenotype in TNBC cells that is mitigated when fibroblast autophagy is blocked. Our work reveals how a metabolic stress response in irradiated fibroblasts and crosstalk with TNBC cells leads to a microenvironment conducive to recurrence. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/492249v3_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@181680aorg.highwire.dtl.DTLVardef@d58a1forg.highwire.dtl.DTLVardef@15f04eforg.highwire.dtl.DTLVardef@13cf5f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

A tumor cell subpopulation of tumor-initiating cells (TIC), or "cancer stem cells", are associated with therapeutic resistance, as well as both local and distant recurrence. Enriched populations of TIC are identified by markers including aldehyde dehydrogenase (ALDH1) activity, the cell surface marker combination CD44+/CD24-, or fluorescent reporters for signaling pathways that regulate TIC function. We showed previously that Signal Transducer and Activator of Transcription (STAT)-mediated transcription allows enrichment for TIC in claudin-low models of human triple-negative breast cancer using a STAT-responsive reporter. However, the molecular phenotypes of STAT TIC are not well understood, and there is no existing method to lineage-trace TIC as they undergo cell state changes. Using a new STAT-responsive lineage-tracing (LT) system in conjunction with our original reporter, we enriched for cells with enhanced mammosphere-forming potential in some, but not all, basal-like triple-negative breast cancer (TNBC) xenograft models (TNBC) indicating TIC-related and TIC-independent functions for STAT signaling. Single-cell RNA sequencing (scRNAseq) of reporter-tagged xenografts and clinical samples identified a common interferon (IFN)/STAT1-associated transcriptional state, previously linked to inflammation and macrophage differentiation, in TIC. Surprisingly, most of the genes we identified are not present in previously published TIC signatures derived using bulk RNA sequencing. Finally, we demonstrated that bone marrow stromal cell antigen 2 (BST2), is a cell surface marker of this state, and that it functionally regulates TIC frequency. These results suggest TIC may exploit the IFN/STAT1 signaling axis to promote their activity, and that targeting this pathway may help eliminate TIC. SignificanceTIC differentially express interferon response genes, which were not previously reported in bulk RNA sequencing-derived TIC signatures, highlighting the importance of coupling single-cell transcriptomics with enrichment to derive TIC signatures.