Reciprocal antagonism of PIN1-APC/CCDH1 governs mitotic protein stability and cell cycle entry.

Induced oncoproteins degradation provides an attractive anti-cancer modality. Activation of anaphase-promoting complex (APC/CCDH1) prevents cell-cycle entry by targeting crucial mitotic proteins for degradation. Phosphorylation of its co-activator CDH1 modulates the E3 ligase activity, but little is known about its regulation after phosphorylation and how to effectively harness APC/CCDH1 activity to treat cancer. Peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 (PIN1)-catalyzed phosphorylation-dependent cis-trans prolyl isomerization drives tumor malignancy. However, the mechanisms controlling its protein turnover remain elusive. Through proteomic screens and structural characterizations, we identify a reciprocal antagonism of PIN1-APC/CCDH1 mediated by domain-oriented phosphorylation-dependent dual interactions as a fundamental mechanism governing mitotic protein stability and cell-cycle entry. Remarkably, combined PIN1 and cyclin-dependent protein kinases (CDKs) inhibition creates a positive feedback loop of PIN1 inhibition and APC/CCDH1 activation to irreversibly degrade PIN1 and other crucial mitotic proteins, which force permanent cell-cycle exit and trigger anti-tumor immunity, translating into synergistic efficacy against triple-negative breast cancer.

Simultaneous isolation of hormone receptor-positive breast cancer organoids and fibroblasts reveals stroma-mediated resistance mechanisms.

Recurrent hormone receptor-positive (HR+) breast cancer kills more than 600,000 women annually. Although HR+ breast cancers typically respond well to therapies, approximately 30% of patients relapse. At this stage, the tumors are usually metastatic and incurable. Resistance to therapy, particularly endocrine therapy is typically thought to be tumor intrinsic (e.g., estrogen receptor mutations). However, tumor-extrinsic factors also contribute to resistance. For example, stromal cells, such as cancer-associated fibroblasts (CAFs), residing in the tumor microenvironment, are known to stimulate resistance and disease recurrence. Recurrence in HR+ disease has been difficult to study due to the prolonged clinical course, complex nature of resistance, and lack of appropriate model systems. Existing HR+ models are limited to HR+ cell lines, a few HR+ organoid models, and xenograft models that all lack components of the human stroma. Therefore, there is an urgent need for more clinically relevant models to study the complex nature of recurrent HR+ breast cancer, and the factors contributing to treatment relapse. Here, we present an optimized protocol that allows a high take-rate, and simultaneous propagation of patient-derived organoids (PDOs) and matching CAFs, from primary and metastatic HR+ breast cancers. Our protocol allows for long-term culturing of HR+ PDOs that retain estrogen receptor expression and show responsiveness to hormone therapy. We further show the functional utility of this system by identifying CAF-secreted cytokines, such as growth-regulated oncogene α , as stroma-derived resistance drivers to endocrine therapy in HR+ PDOs.

Estrogen Receptor Alpha and ESR1 Mutations in Breast Cancer.

The estrogen receptor alpha (ERα) is a nuclear transcription factor that is expressed in more than 70% of all breast cancers. Key genes involved in proliferation and tumor progression are transcriptionally regulated by ERα making it an important therapeutic target. Indeed, the first class of targeted treatments in cancer are endocrine treatments that target ERα either by competitive inhibition, reduced ligand production or receptor degradation. Despite the efficacy of these drugs, resistance to endocrine treatment remains a key clinical challenge. Only about 50% of patients treated with endocrine treatment in early-stage disease will benefit from adjuvant endocrine treatment and nearly all patients treated in the metastatic setting will develop disease progression while on endocrine treatment. Multiple mechanisms of resistance to endocrine treatment have been identified in pre-clinical models and clinical samples. These include both intrinsic (de novo) mechanisms and adaptive, acquired mechanisms. Over the past few years, gain-of-function missense mutations of ESR1, the gene encoding ERα, have been unveiled and identified as the most common genomic mechanism of acquired resistance to endocrine treatments. These mutations are clustered in a "hot spot" region within the ligand binding domain and engender constitutive, ligand-independent activity. Clinical studies evaluating these ESR1 mutations in metastatic ERα positive breast cancer demonstrate decreased overall survival which also highlights their prognostic role. In this chapter, we will provide a detailed review of structural and biophysical characteristics, functional consequences and clinical implications of the ESR1 mutations. We will also discuss potential therapeutic strategies to overcome treatment resistance in the context of ESR1 mutations and implications for future treatment selection.

Lineage-specific silencing of PSAT1 induces serine auxotrophy and sensitivity to dietary serine starvation in luminal breast tumors.

A major challenge of targeting metabolism for cancer therapy is pathway redundancy, in which multiple sources of critical nutrients can limit the effectiveness of some metabolism-targeted therapies. Here, we analyze lineage-dependent gene expression in human breast tumors to identify differences in metabolic gene expression that may limit pathway redundancy and create therapeutic vulnerabilities. We find that the serine synthesis pathway gene PSAT1 is the most depleted metabolic gene in luminal breast tumors relative to basal tumors. Low PSAT1 prevents de novo serine biosynthesis and sensitizes luminal breast cancer cells to serine and glycine starvation in vitro and in vivo. This PSAT1 expression disparity preexists in the putative cells of origin of basal and luminal tumors and is due to luminal-specific hypermethylation of the PSAT1 gene. Our data demonstrate that luminal breast tumors are auxotrophic for serine and may be uniquely sensitive to therapies targeting serine availability.

The CoVID-TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID-19.

BACKGROUND: Hospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking. OBJECTIVES: To assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19. METHODS: Among patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap. RESULTS: From March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission. CONCLUSIONS: Hospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Time to SARS-CoV-2 clearance among patients with cancer and COVID-19.

BACKGROUND: For cancer patients, coronavirus disease 19 (COVID-19) infection can lead to delays in cancer therapy both due to the infection itself and due to the need to minimize exposure to other patients and to staff. Clearance guidelines have been proposed, but expected time to clearance has not been established. METHODS: We identified all patients at a tertiary care hospital cancer center between 25 March 2020 and 6 June 2020 with a positive nasopharyngeal reverse transcriptase polymerase chain reaction (RT-PCR) test for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a cancer-related visit within 3 years, and at least one follow-up assay. We determined the time to clearance using American Society of Clinical Oncology (ASCO), the UK National Institute for Health and Care Excellence (UK-NICE), and Centers for Disease Control and Prevention (CDC) criteria. A matched non-cancer comparison cohort was also identified. RESULTS: Thirty-two cancer patients were identified. Nineteen were cleared by ASCO criteria, with estimated median time to clearance of 50 days. Fourteen patients resumed chemotherapy prior to clearance. Using UK-NICE criteria, median time to clearance would have been 31 days, and using CDC criteria, it would have been 13 days. The matched non-cancer cohort had similar clearance time, but with less frequent testing. CONCLUSION: SARS-CoV-2 clearance times differ substantially depending on the criteria used and may be prolonged in cancer patients. This could lead to a delay in cancer care, increased use of clearance testing, and extension of infection control precautions.

Outcomes of COVID-19 in Patients With a History of Cancer and Comorbid Cardiovascular Disease.

BACKGROUND: Cancer and cardiovascular disease (CVD) are independently associated with adverse outcomes in patients with COVID-19. However, outcomes in patients with COVID-19 with both cancer and comorbid CVD are unknown. METHODS: This retrospective study included 2,476 patients who tested positive for SARS-CoV-2 at 4 Massachusetts hospitals between March 11 and May 21, 2020. Patients were stratified by a history of either cancer (n=195) or CVD (n=414) and subsequently by the presence of both cancer and CVD (n=82). We compared outcomes between patients with and without cancer and patients with both cancer and CVD compared with patients with either condition alone. The primary endpoint was COVID-19-associated severe disease, defined as a composite of the need for mechanical ventilation, shock, or death. Secondary endpoints included death, shock, need for mechanical ventilation, need for supplemental oxygen, arrhythmia, venous thromboembolism, encephalopathy, abnormal troponin level, and length of stay. RESULTS: Multivariable analysis identified cancer as an independent predictor of COVID-19-associated severe disease among all infected patients. Patients with cancer were more likely to develop COVID-19-associated severe disease than were those without cancer (hazard ratio [HR], 2.02; 95% CI, 1.53-2.68; P<.001). Furthermore, patients with both cancer and CVD had a higher likelihood of COVID-19-associated severe disease compared with those with either cancer (HR, 1.86; 95% CI, 1.11-3.10; P=.02) or CVD (HR, 1.79; 95% CI, 1.21-2.66; P=.004) alone. Patients died more frequently if they had both cancer and CVD compared with either cancer (35% vs 17%; P=.004) or CVD (35% vs 21%; P=.009) alone. Arrhythmias and encephalopathy were also more frequent in patients with both cancer and CVD compared with those with cancer alone. CONCLUSIONS: Patients with a history of both cancer and CVD are at significantly higher risk of experiencing COVID-19-associated adverse outcomes. Aggressive public health measures are needed to mitigate the risks of COVID-19 infection in this vulnerable patient population.

Time to SARS-CoV-2 Clearance Among Patients with Cancer and COVID-19.

QUESTION: What is the median time to clearance of SARS-CoV-2 among cancer patients according to currently used criteria? FINDINGS: In this single-institution retrospective cohort study, the median time to SARS-CoV-2 clearance was 50 days using the ASCO/CDC criteria of 2 negative RT-PCR assays >24 hours apart. Using alternative criteria of 1 negative RT-PCR assay (UK-NICE) or CDC clinical criteria (10 days after first positive RT-PCR and 3 days after last symptoms), median clearance times were 31 days and 13 days, respectively. Meaning: SARS-CoV-2 clearance times differ substantially depending on criteria used and may be prolonged in cancer patients.

Retinoblastoma protein expression and its predictors in triple-negative breast cancer.

Retinoblastoma protein (Rb) is a product of the RB tumor suppressor gene. Its expression is highly prevalent in luminal breast cancers and is critical to the success of cyclin-dependent kinase (CDK) 4/6 inhibitor therapy. Expression of Rb in triple-negative breast cancer (TNBC), tumors generally associated with basal biology, is not well known. However, heterogeneity among TNBC and presence of subtypes with luminal features are well described. The purpose of this study was to determine prevalence and predictors of Rb protein expression in BRCA1-associated and sporadic TNBCs. We studied 180 TNBC patients (70 BRCA1-associated and 110 sporadic). The clinical and pathologic features of these cases were previously assessed and reported. For this study, immunohistochemical stains for Rb were performed on tissue microarray sections. Details of treatment and outcome were abstracted from medical records. Fifty-one percent of TNBC were Rb positive (≥10% nuclei staining), and 85% of these cases had ≥50% nuclei staining. Rb expression was significantly associated with sporadic TNBC (71.4% vs 49.4%; p < 0.001), androgen receptor (AR) expression (16.5% vs 3.4%; p = 0.007), histologic grade 1 or 2 (9.9% vs 2.2%; p = 0.04), and first recurrence in bone (8.8% vs 1.1%; p = 0.03). Expression of p53 was not associated with Rb expression. Expression of Rb in TNBC was significantly associated with sporadic TNBC, AR expression, lower histologic grade, and metastasis to bone. These observations characterize a TNBC subtype with features suggestive of luminal-like biology and the potential to benefit from CDK 4/6 inhibition.

Computational Optics Enables Breast Cancer Profiling in Point-of-Care Settings.

The global burden of cancer, severe diagnostic bottlenecks in underserved regions, and underfunded health care systems are fueling the need for inexpensive, rapid, and treatment-informative diagnostics. On the basis of advances in computational optics and deep learning, we have developed a low-cost digital system, termed AIDA (artificial intelligence diffraction analysis), for breast cancer diagnosis of fine needle aspirates. Here, we show high accuracy (>90%) in (i) recognizing cells directly from diffraction patterns and (ii) classifying breast cancer types using deep-learning-based analysis of sample aspirates. The image algorithm is fast, enabling cellular analyses at high throughput (∼3 s per 1000 cells), and the unsupervised processing allows use by lower skill health care workers. AIDA can perform quantitative molecular profiling on individual cells, revealing intratumor molecular heterogeneity, and has the potential to improve cancer diagnosis and treatment. The system could be further developed for other cancers and thus find widespread use in global health.

A lupus anti-DNA autoantibody mediates autocatalytic, targeted delivery of nanoparticles to tumors.

Strategies to target nanoparticles to tumors that rely on surface modification with ligands that bind molecules overexpressed on cancer cells or the tumor neovasculature suffer from a major limitation: with delivery of toxic agents the amount of molecules available for targeting decreases with time; consequently, the efficiency of nanoparticle delivery is reduced. To overcome this limitation, here we propose an autocatalytic tumor-targeting mechanism based on targeting extracellular DNA (exDNA). exDNA is enriched in the tumor microenviroment and increases with treatment with cytotoxic agents, such as doxorubicin (DOX), due to release of DNA by dying tumor cells. We tested this approach using poly(lactic-co-glycolic acid) (PLGA) nanoparticles surface-conjugated with fragments of 3E10 (3E10EN), a lupus anti-DNA autoantibody. We demonstrated that 3E10EN-conjugated nanoparticles bound to DNA and preferentially localized to tumors in vivo. The efficiency of tumor localization of 3E10EN-conjugated, DOX-loaded nanoparticles increased with time and subsequent treatments, demonstrating an autocatalytic effect. 3E10EN-conjugated DOX-loaded nanoparticles exhibited a significant anti-tumor effect that was superior to all controls. This work demonstrates the promise of autocatalytic drug delivery mechanisms and establishes proof of concept for a new anti-DNA autoantibody-based approach for enhancing delivery of nanoparticles to tumors.

Mutation based treatment recommendations from next generation sequencing data: a comparison of web tools.

Interpretation of complex cancer genome data, generated by tumor target profiling platforms, is key for the success of personalized cancer therapy. How to draw therapeutic conclusions from tumor profiling results is not standardized and may vary among commercial and academically-affiliated recommendation tools. We performed targeted sequencing of 315 genes from 75 metastatic breast cancer biopsies using the FoundationOne assay. Results were run through 4 different web tools including the Drug-Gene Interaction Database (DGidb), My Cancer Genome (MCG), Personalized Cancer Therapy (PCT), and cBioPortal, for drug and clinical trial recommendations. These recommendations were compared amongst each other and to those provided by FoundationOne. The identification of a gene as targetable varied across the different recommendation sources. Only 33% of cases had 4 or more sources recommend the same drug for at least one of the usually several altered genes found in tumor biopsies. These results indicate further development and standardization of broadly applicable software tools that assist in our therapeutic interpretation of genomic data is needed. Existing algorithms for data acquisition, integration and interpretation will likely need to incorporate artificial intelligence tools to improve both content and real-time status.

Melphalan, lenalidomide and dexamethasone for the treatment of immunoglobulin light chain amyloidosis: results of a phase II trial.

We report results of a phase II trial of combination of melphalan, lenalidomide, and dexamethasone for the treatment of immunoglobulin light chain (AL) amyloidosis. The primary objectives were tolerability and hematologic response rate; secondary objectives were organ responses and survival. Treatment protocol consisted of melphalan 5 mg/m(2)/day for four days, lenalidomide 10 mg/day for 21 days and dexamethasone 20-40 mg once a week every 28 days for a total of 12 cycles. Sixteen subjects were enrolled of whom 14 completed at least 3 cycles and were evaluable for response. Grade 3/4 toxicities were experienced by 88% (n=14), the most common being myelosuppression (n=7). Dose reductions occurred in 85% (n=12 of 14) of subjects. Hematologic partial and complete responses were achieved by 43% (n=6 of 14) and 7% (n=1 of 14), respectively. The median overall survival has not been reached and median progression-free survival is 24 months. In conclusion, this combination is associated with significant myelosuppression leading to dose modifications and producing minor hematologic responses in AL amyloidosis. http://clinicaltrials.gov/ct2/show/NCT00679367.