Immunologically "cold" triple negative breast cancers engraft at a higher rate in patient derived xenografts

Hope for OTHERS (Our Tissue Helping Enhance Research & Science): research results from the University of Pittsburgh rapid autopsy program for breast cancer

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.

Reprogramming the breast tumor immune microenvironment: cold-to-hot transition for enhanced immunotherapy

This review discusses reprogramming the breast tumor immune microenvironment from an immunosuppressive cold state to an immunologically active hot state. A complex interplay is revealed, in which the accumulation of metabolic byproducts-such as lactate, reactive oxygen species (ROS), and ammonia-is shown to impair T-cell function and promote tumor immune escape. It is demonstrated that the tumor microenvironment (TME) is dominated by immunosuppressive cytokines, including interleukin-10 (IL-10), transforming growth factorβ (TGFβ), and IL-35. Notably, IL-35 is produced by regulatory T cells and breast cancer cells. The conversion of conventional T cells into IL-35-producing induced regulatory T cells, along with the inhibition of pro-inflammatory cytokine secretion, contributes to the suppression of anti-tumor immunity. It is further demonstrated that key immune checkpoint molecules-such as PD-1, PDL1, CTLA-4, TIM-3, LAG-3, and TIGIT-are upregulated within the TME, leading to Tcell exhaustion and diminished immune responses. The blockade of these checkpoints is shown to restore T-cell functionality and is proposed as a strategy to convert cold tumors into hot ones with robust effector cell infiltration. The therapeutic potential of chimeric antigen receptor (CAR)T cell therapy is also explored, and targeting specific tumor-associated antigens, such as glycoproteins and receptor tyrosine kinases, is highlighted. It is suggested that CART cell efficacy can be enhanced by combining these cells with immune checkpoint inhibitors and other immunomodulatory agents, thereby overcoming the barriers imposed by the immunosuppressive TME. Moreover, the role of the microbiome in regulating estrogen metabolism and systemic inflammation is reviewed. Alterations in the gut microbiota are shown to affect the TME, and microbiome-based interventions are proposed as an additional means to facilitate the cold-to-hot transition. It is concluded that by targeting the metabolic and immunological pathways that underpin immune suppression-through combination strategies involving checkpoint blockade, CART cell therapies, and microbiome modulation-the conversion of the breast TME from cold to hot can be achieved. This reprogramming is anticipated to enhance immune cell infiltration and function, thereby improving the overall efficacy of immunotherapies and leading to better clinical outcomes for breast cancer patients.

Hope for Others: Research Results from the University of Pittsburgh Rapid Autopsy Program for Breast Cancer

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.

Steroid hormone receptors, exome sequencing and treatment responsiveness of breast cancer patient-derived xenografts originated in a South American country

Breast cancer (BC) patient-derived xenografts (PDX) are relevant models for precision medicine. However, there are no collections derived from South American BC patients. Since ethnicity significantly impacts clinical outcomes, it is necessary to develop PDX models from different lineages. Our goals were to a) develop BC PDX from our population; b) characterize the expression of estrogen (ER), progesterone (PR), androgen (AR) and glucocorticoid (GR) receptors, basal and luminal cytokeratins, EGFR and HER2; c) identify PDX mutations; d) evaluate the response to treatments selected based on their biological and genetic features, and e) perform BC tissue cultures (BCTC) from PDX tissues and compare in vivo and ex vivo results. Surgical fragments were maintained in a culture medium and inoculated subcutaneously into untreated NSG female mice, or treated with estradiol pellets. Other fragments were fixed in formalin for diagnosis and immunohistochemistry, and a third piece was frozen at -80°C for molecular studies or whole exome-sequencing. Tumors were serially transplanted into NSG mice. Once the PDX was established, in vivo and ex vivo drug responses were evaluated. Eight PDX were established: two ER + [BC-AR685 (PR +) and BC-AR707 (PR-)], one from a triple-negative (TN) recurrence whose primary tumor was ER + (BC-AR485), one HER2 + (BC-AR474) and four TN primary tumors (BC-AR553, BC-AR546, BC-AR631 and BC-AR687). BC-AR685 had higher levels of PR isoform A than isoform B and was sensitive to mifepristone, tamoxifen, and palbociclib. BC-AR707 was inhibited by tamoxifen and testosterone. BC-AR474 was inhibited by trastuzumab and trastuzumab emtansine. BC-AR485 was sensitive to doxorubicin and resistant to paclitaxel in vivo and ex vivo. BC-AR687 carried a PIK3CA (C420R) mutation and was sensitive to alpelisib and mTOR inhibitors. All PDX expressed AR with varying intensities. GR and AR were co-expressed in the ER + tumors and in 3 TN PDX. We report the first PDX originated from South American countries that were genetically and biologically characterized and may be used in precision medicine studies. PDX expressing AR and/or GR are powerful tools to evaluate different endocrine treatment combinations even in TN tumors.

Patient-Derived Xenografts of Triple-Negative Breast Cancer Enable Deconvolution and Prediction of Chemotherapy Responses

Combination chemotherapy remains essential for clinical management of triple-negative breast cancer (TNBC). Consequently, responses to multiple single agents cannot be delineated at the single patient level, even though some patients might not require all drugs in the combination. Herein, we conduct multi-omic analyses of orthotopic TNBC patient-derived xenografts (PDXs) treated with single agent carboplatin, docetaxel, or the combination. Combination responses were usually no better than the best single agent, with enhanced response in only ~13% of PDX, and apparent antagonism in a comparable percentage. Single-omic comparisons showed largely non-overlapping results between genes associated with single agent and combination treatments that could be validated in independent patient cohorts. Multi-omic analyses of PDXs identified agent-specific biomarkers/biomarker combinations, nominating high Cytokeratin-5 (KRT5) as a general marker of responsiveness. Notably, integrating proteomic with transcriptomic data improved predictive modeling of pathologic complete response to combination chemotherapy. PDXs refractory to all treatments were enriched for signatures of dysregulated mitochondrial function. Targeting this process indirectly in a PDX with HDAC inhibition plus chemotherapy in vivo overcomes chemoresistance. These results suggest possible resistance mechanisms and therapeutic strategies in TNBC to overcome chemoresistance, and potentially allow optimization of chemotherapeutic regimens.

PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks

MOTIVATION

Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses.

RESULTS

To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user's needs based on the reliability of imputed values.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/DILiS-lab/pepermint.

Assessment of Patient-Derived Xenograft Growth and Antitumor Activity: The NCI PDXNet Consensus Recommendations

Although patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Landscape of unconventional γδ T cell subsets in cancer

T cells are broadly categorized into two groups, namely conventional and unconventional T cells. Conventional T cells are the most prevalent and well-studied subset of T cells. On the other hand, unconventional T cells exhibit diverse functions shared between innate and adaptive immune cells. During recent decades, γδ T cells have received attention for their roles in cancer immunity. These cells can detect various molecules, such as lipids and metabolites. Also, they are known for their distinctive ability to recognize and target cancer cells in the tumor microenvironment (TME). This feature of γδ T cells could provide a unique therapeutic tool to fight against cancer. Understanding the role of γδ T cells in TME is essential to prepare the groundwork to use γδ T cells for clinical purposes. Here, we provide recent knowledge regarding the role γδ T cell subsets in different cancer types.

Modeling the Tumor Microenvironment and Cancer Immunotherapy in Next-Generation Humanized Mice

Cancer immunotherapy has brought significant clinical benefits to numerous patients with malignant disease. However, only a fraction of patients experiences complete and durable responses to currently available immunotherapies. This highlights the need for more effective immunotherapies, combination treatments and predictive biomarkers. The molecular properties of a tumor, intratumor heterogeneity and the tumor immune microenvironment decisively shape tumor evolution, metastasis and therapy resistance and are therefore key targets for precision cancer medicine. Humanized mice that support the engraftment of patient-derived tumors and recapitulate the human tumor immune microenvironment of patients represent a promising preclinical model to address fundamental questions in precision immuno-oncology and cancer immunotherapy. In this review, we provide an overview of next-generation humanized mouse models suitable for the establishment and study of patient-derived tumors. Furthermore, we discuss the opportunities and challenges of modeling the tumor immune microenvironment and testing a variety of immunotherapeutic approaches using human immune system mouse models.

Unique sphingolipid signature identifies luminal and triple-negative breast cancer subtypes

Breast cancer (luminal and triple-negative breast cancer [TNBC]) is the most common cancer among women in India and worldwide. Altered sphingolipid levels have emerged as a common phenomenon during cancer progression. However, these alterations are yet to be translated into robust diagnostic and prognostic markers for cancer. Here, we present the quantified sphingolipids of tumor and adjacent-normal tissues from patients of luminal (n = 70) and TNBC (n = 42) subtype from an Indian cohort using targeted liquid chromatography mass spectrometry. We recorded unique sphingolipid profiles that distinguished luminal and TNBC tumors in comparison to adjacent normal tissue by six-sphingolipid signatures. Moreover, systematic comparison of the profiles of luminal and TNBC tumors provided a unique five-sphingolipid signature distinguishing the two subtypes. We further identified key sphingolipids that can stratify grade II and grade III tumors of luminal and TNBC subtype as well as their lymphovascular invasion status. Therefore, we provide the right evidence to develop these candidate sphingolipids as widely acceptable marker/s capable of diagnosing luminal vs TNBC subtype of breast cancer, and predicting the disease severity by identifying the tumor grade.