Tumor heterogeneity represents a major challenge in breast cancer, being associated with disease progression and treatment resistance. Precision medicine has been extensively applied to dissect tumor heterogeneity and, through a deeper molecular understanding of the disease, to personalize therapeutic strategies. In the last years, technological advances have widely improved the understanding of breast cancer biology and several trials have been developed to translate these new insights into clinical practice, with the ultimate aim of improving patients' outcomes. In the era of molecular oncology, genomics analyses and other methodologies are shaping a new treatment algorithm in breast cancer care. In this manuscript, we review the main steps of precision medicine to predict drug sensitivity in breast cancer from a translational point of view. Genomic developments and their clinical implications are discussed, along with technological advancements that could broaden precision medicine applications. Current achievements are put into perspective to provide an overview of the state-of-art of breast cancer precision oncology as well as to identify future research directions.

Although immune checkpoint inhibitors can lead to “exceptional”, durable clinical responses in a subset of patients, the molecular mechanisms underlying exceptional responses (ERs) are still unknown. We analyzed pre-therapy genomic and transcriptomic data in treatment-naive patients with mccRCC treated with standard-of-care immunotherapies: (1) combination of PD-1/PDL1 and CTLA-4 (IO/IO), or (2) PD-1/PD-L1 inhibitor and VEGF-receptor inhibitor (IO/VEGF) combination. In the IO/IO cohort, clonal neoantigen load was significantly higher in ER patients. In the IO/VEGF cohort, ER patients displayed strong enrichment of B-cell receptor signaling related pathways, tertiary lymphoid structure (TLS) signatures, and evidence of increased metabolic activity. Our results suggest that ER may be related to clonal neoantigen-driven cytotoxic T-cell responses and TLS formation in tumor microenvironments. We conclude that novel therapeutic combinations that elicit both T- and B-cell directed anti-tumor immunity may be important to achieve exceptional benefit to IO-based treatment in ccRCC.

Cytokine storm due to COVID-19 can cause high morbidity and mortality. Patients with cancer treated with immunotherapy (IO) and those with immunosuppression may have higher rates of cytokine storm due to immune dysregulation. We sought to evaluate the association of IO and immunosuppression with COVID-19 outcomes and cytokine storm occurrence among patients with cancer and COVID-19, based on data from the COVID-19 and Cancer Consortium (CCC19). A registry-based retrospective cohort study was conducted on patients reported to the CCC19 registry from March 2020 to September 2021. The primary outcome was defined as an ordinal scale of COVID-19 severity. The secondary outcome was the occurrence of a cytokine storm using CCC19 variables, defined as biological and clinical evidence of severe inflammation, with end-organ dysfunction (Fajgenbaum D.C. et al., N Engl J Med., 2020). The association of IO or immunosuppression with the outcomes of interest were evaluated using a multivariable logistic regression balanced for covariate distributions through inverse probability of treatment weighting (IPTW). A total of 10,214 patients were included, among which 482 (4.7%) received IO, 3,715 (36.4%) received non-IO systemic therapies, and 6,017 (58.9%) were untreated in the 3 months prior to COVID-19 diagnosis. No difference in COVID-19 severity or the development of a cytokine storm was found in the IO group compared to the untreated group (aOR: 0.77; 95%CI:0.45-1.32, and aOR: 1.06; 95%CI:0.42-2.67, respectively). On multivariable analysis, baseline immunosuppression was associated with worse outcomes both in relation to COVID-19 severity (aOR: 1.89; 95%CI:1.51-2.35) and the presence of a cytokine storm (aOR: 1.75; 95%CI:1.30-2.35). Administration of IO was not associated with severe outcomes in patients with cancer and COVID-19, whereas pre-existing baseline immunosuppression appears to be independently associated with worse clinical outcomes including cytokine storm.

Background: COVID-19 disproportionately impacted patients with cancer as a result of direct infection, and delays in diagnosis and therapy. Oncological clinical trials are resource-intensive endeavors that could be particularly susceptible to disruption by the pandemic, but few studies have evaluated the impact of the pandemic on clinical trial conduct. Patients and methods: This prospective, multicenter study assesses the impact of the pandemic on therapeutic clinical trials at two large academic centers in the Northeastern United States between December 2019 and June 2021. The primary objective was to assess the enrollment on, accrual to, and activation of oncology therapeutic clinical trials during the pandemic using an institution-wide cohort of (i) new patient accruals to oncological trials, (ii) a manually curated cohort of patients with cancer, and (ii) a dataset of new trial activations. Results: The institution-wide cohort included 4756 new patients enrolled to clinical trials from December 2019 to June 2021. A major decrease in the numbers of new patient accruals (-46%) was seen early in the pandemic, followed by a progressive recovery and return to higher-than-normal levels (+2.6%). A similar pattern (from -23.6% to +30.4%) was observed among 467 newly activated trials from June 2019 to June 2021. A more pronounced decline in new accruals was seen among academically sponsored trials (versus industry sponsored trials) (P < 0.05). In the manually curated cohort, which included 2361 patients with cancer, non-white patients tended to be more likely taken off trial in the early pandemic period (adjusted odds ratio: 2.60; 95% confidence interval 1.00-6.63), and substantial pandemic-related deviations were recorded. Conclusions: Substantial disruptions in clinical trial activities were observed early during the pandemic, with a gradual recovery during ensuing time periods, both from an enrollment and an activation standpoint. The observed decline was more prominent among academically sponsored trials, and racial disparities were seen among people taken off trial.

Patients with cancer are at higher risk of dying of COVID-19. Known risk factors for 30-day all-cause mortality (ACM-30) in patients with cancer are older age, sex, smoking status, performance status, obesity, and co-morbidities. We hypothesized that common clinical and laboratory parameters would be predictive of a higher risk of 30-day ACM, and that a machine learning approach (random forest) could produce high accuracy. In this multi-institutional COVID-19 and Cancer Consortium (CCC19) registry study, 12,661 patients enrolled between March 17, 2020 and December 31, 2021 were utilized to develop and validate a model of ACM-30. ACM-30 was defined as death from any cause within 30 days of COVID-19 diagnosis. Pre-specified variables were: age, sex, race, smoking status, ECOG performance status (PS), timing of cancer treatment relative to COVID19 diagnosis, severity of COVID19, type of cancer, and other laboratory measurements. Missing variables were imputed using random forest proximity. Random forest was utilized to model ACM-30. The area under the curve (AUC) was computed as a measure of predictive accuracy with out-of-bag prediction. One hundred bootstrapped samples were used to obtain the standard error of the AUC. The median age at COVID-19 diagnosis was 65 years, 53% were female, 18% were Hispanic, and 16.7% were Black. Over half were never smokers and the median body mass index was 28.2. Random forest with under sampling selected 20 factors prognostic of ACM-30. The AUC was 88.9 (95% CI 88.5-89.2). Highly informative parameters included: COVID-19 severity at presentation, cancer status, age, troponin level, ECOG PS and body mass index. This prognostic model based on readily available clinical and laboratory values can be used to estimate individual survival probability within 30-days for COVID-19. In addition, this model can be used to select or classify patients with cancer and COVID-19 into risk groups based on validated cut points, for treatment selection, prophylaxis prioritization, and/or enrollment in clinical trials. Future work includes external validation using other large datasets of patients with COVID-19 and cancer.

Response to ICI per RECIST 1.1 is durable and associated with improved overall survival (OS). Benefit extends beyond responders to pts with stable disease (SD), who constitute a heterogeneous group with increase or decrease in tumor size without meeting response or progression criteria. We hypothesize that ART may better discriminate benefit translating to improved OS. We included 1216 pts with advanced non-small cell lung cancer (NSCLC), melanoma, urothelial carcinoma (UC), esophagogastric cancer (EGC), renal cell carcinoma (RCC), head and neck squamous cell cancer (HNSCC), and colorectal cancer (CRC) treated with ICI. Using Neutrophil-Lymphocyte Ratio (NLR), sites of metastasis, ECOG-performance status, tumor mutation burden, and prior therapy, we conducted Cox proportional hazards models to examine association of ART and RECIST1.1 with OS. Results were validated using bootstrap analyses of 1000 replicates. We included pts with NSCLC (n=522), melanoma (n=262), UC (n=140), EGC (n=93), RCC (n=83), HNSCC (n=71) and CRC (n=43). Median age was 66 (range 58-73) and 58% were male. Per RECIST, 68 pts (5.6%) had complete response (CR), 277 (22.9%) partial response (PR), 579 (47.7%) progressive disease (PD), and 290 (23.9%) SD. 175 (60.3%) pts with SD had ART. CR+PR (vs SD+PD) was associated with OS (HR=0.3, 95%CI: 0.2-0.4, p<0.001). ART (vs no ART) was significantly prognostic for pts with SD on univariate (HR=0.3, 95%CI: 0.2-0.3, p<0.001) and multivariate analysis (Table). Bootstrap validation corroborated these results (HR=0.5, 95%CI: 0.4-0.7, p<0.001) in NSCLC, melanoma, and UC where >25 pts had SD.Table: 116PVariableHR (95% CI)p-valueECOG1.6 (1.1-2.2)0.009NLR1.4 (1.1-1.7)0.003Bone Metastasis0.7 (0.5-0.9)0.013ART0.5 (0.3-0.6)<0.001 Open table in a new tab ART is a robust endpoint capturing OS benefit in a larger proportion of pts with advanced solid tumors receiving ICI compared to RECIST 1.1. ART warrants study as a surrogate endpoint in pts without RECIST-measurable disease to enable inclusive trials that expedite drug development and biomarker discovery.

Background: Vaccination is an important preventive health measure to protect against symptomatic and severe COVID-19. Impaired immunity secondary to an underlying malignancy or recent receipt of antineoplastic systemic therapies can result in less robust antibody titers following vaccination and possible risk of breakthrough infection. As clinical trials evaluating COVID-19 vaccines largely excluded patients with a history of cancer and those on active immunosuppression (including chemotherapy), limited evidence is available to inform the clinical efficacy of COVID-19 vaccination across the spectrum of patients with cancer. Patients and methods: We describe the clinical features of patients with cancer who developed symptomatic COVID-19 following vaccination and compare weighted outcomes with those of contemporary unvaccinated patients, after adjustment for confounders, using data from the multi-institutional COVID-19 and Cancer Consortium (CCC19). Results: Patients with cancer who develop COVID-19 following vaccination have substantial comorbidities and can present with severe and even lethal infection. Patients harboring hematologic malignancies are over-represented among vaccinated patients with cancer who develop symptomatic COVID-19. Conclusions: Vaccination against COVID-19 remains an essential strategy in protecting vulnerable populations, including patients with cancer. Patients with cancer who develop breakthrough infection despite full vaccination, however, remain at risk of severe outcomes. A multilayered public health mitigation approach that includes vaccination of close contacts, boosters, social distancing, and mask-wearing should be continued for the foreseeable future.

In total hip arthroplasty, the minimum host bone coverage required on the cup for stable fixation has been previously reported; however, the coverage was generally evaluated on a 2-dimensional (2D) image and 3-dimensional (3D) coverage has not been well described.We used postoperative computed tomography images to retrospectively measure 3D cup coverage in 151 hips with developmental dysplasia of the hip that underwent primary total hip arthroplasty. The aims were to (1) determine the minimum requirement of the 3D coverage for stable cup fixation; (2) evaluate the relationship between 2D and 3D coverage; and (3) identify the factors associated with 2D-3D discrepancy, defined as follows: 2D-3D discrepancy = 2D coverage−3D coverage.All cups showed stable fixation as demonstrated by bone ingrowth with an average postoperative period of 48 months. The minimum 3D coverage was 61.2%, with a mean value of 77.1% ± 6.7% and maximum value of 97.6%. We found a significant positive but poor relationship between 3D and 2D coverage (bone coverage index; r = 0.30). Consequently, the 2D-3D discrepancy varied greatly, with a mean value of −1.6% ± 12.3% (range, −36.5% to 32.2%). Multiple linear regression analyses confirmed that lower cup center and anterior tilt of the pelvis relative to the computed tomography table were independent factors associated with increasing 2D-3D discrepancy.This study showed that 2D coverage, measured on a projected image, carries a risk of overestimation, especially in hips with the aforementioned features.

Pacemaker implantation in infants is limited to epicardial lead placement and an abdominal generator pocket. We propose a minimally invasive solution using a prototype miniature pacemaker with a steroid-eluting leadlet that can affix against the epicardium under thoracoscopy.The purpose of this study was to evaluate the safety and feasibility of acute implantation of a prototype miniature pacemaker in an infant porcine model.A self-anchoring 2-channel access port was inserted into a 1-cm incision left of the subxiphoid space. A rigid thoracoscope with variable viewing angle was inserted through the main channel to visualize the heart under insufflation. An 18-G needle through the second channel accessed the pericardial space, which was secured with a 7-F sheath. The leadlet was affixed against the epicardium using a distal helical side-biting electrode. The sheath, thoracoscope, and port were removed, and the pacemaker was tucked into the incision. Ventricular sensing, lead impedances, and capture thresholds were measured.Twelve piglets (weight 4.8 ± 1.9 kg) had successful device implantation. The median time from incision to leadlet fixation was 21 minutes (interquartile range [IQR] 18–31 minutes). The median lead impedance was 510 Ω (IQR 495–620 Ω). The median R-wave amplitude was 5.7 mV (IQR 4.2–7.0 mV). The median capture threshold was 1.63 V (IQR 1.32–2.97 V) at 0.4 ms pulse width and 1.50 V (IQR 1.16–2.38 V) at 1.0 ms pulse width. There were no complications.Minimally invasive epicardial placement of a prototype miniature pacemaker under thoracoscopy was safe and avoided open chest surgery and creation of an abdominal generator pocket.

Sports performance is generally considered to be governed by a range of interacting physiological, biomechanical, and psychological variables, amongst others. Despite sports performance being multi-factorial, however, the majority of performance-oriented sports science research has predominantly been monodisciplinary in nature, presumably due, at least in part, to the lack of a unifying theoretical framework required to integrate the various subdisciplines of sports science. In this target article, I propose a Grand Unified Theory (GUT) of sports performance—and, by elaboration, sports science—based around the constraints framework introduced originally by Newell (1986). A central tenet of this GUT is that, at both the intra- and inter-individual levels of analysis, patterns of coordination and control, which directly determine the performance outcome, emerge from the confluence of interacting organismic, environmental, and task constraints via the formation and self-organisation of coordinative structures. It is suggested that this GUT could be used to: foster interdisciplinary research collaborations; break down the silos that have developed in sports science and restore greater disciplinary balance to the field; promote a more holistic understanding of sports performance across all levels of analysis; increase explanatory power of applied research work; provide stronger rationale for data collection and variable selection; and direct the development of integrated performance monitoring technologies. This GUT could also provide a scientifically rigorous basis for integrating the subdisciplines of sports science in applied sports science support programmes adopted by high-performance agencies and national governing bodies for various individual and team sports.