Primary analysis of MOUNTAINEER: A phase 2 study of tucatinib and trastuzumab for HER2-positive mCRC

Background: Despite the occurrence of HER2 amplification/overexpression (HER2+) in ~3% to 5% of all patients with metastatic colorectal cancer (mCRC) and up to ~10% of patients with RAS/BRAF wild-type mCRC, there are currently no FDA- or EMA-approved HER2-directed therapies for HER2+ mCRC. Patients with mCRC who progress on early lines of chemotherapy regimens receive limited clinical benefit from current standard-of-care treatments. Tucatinib is a highly selective, HER2-directed, tyrosine kinase inhibitor. The MOUNTAINEER trial (NCT03043313) was initiated to evaluate the efficacy and safety of the investigational combination of tucatinib with trastuzumab in patients with HER2+ mCRC. Here we present results from the primary analysis of MOUNTAINEER. Methods: MOUNTAINEER is a multi-center, open-label, randomised, phase 2 trial conducted in the US and Europe. Eligible patients had HER2+ (one or more local tests: 3+ immunohistochemistry, 2+ immunohistochemistry with amplification by in situ hybridization, or amplification by next‑generation sequencing of tumor tissue) and RAS wild-type mCRC with progression on or intolerance to fluoropyrimidine, oxaliplatin, irinotecan, and an anti-VEGF antibody. Measurable disease and an ECOG performance status of 0–2 were required. Previous HER2-directed therapies were not permitted. The trial initially consisted of a single cohort (Cohort A) to be treated with tucatinib (300 mg PO BID) and trastuzumab (8 mg/kg IV then 6 mg/kg IV every 3 weeks). The trial was expanded to include patients randomised 4:3 to receive tucatinib + trastuzumab (Cohort B) or tucatinib monotherapy (Cohort C). The primary endpoint is confirmed objective response rate (ORR) per RECIST 1.1 by blinded independent central review (BICR) in Cohorts A+B. Secondary endpoints include duration of response (DOR), progression-free survival (PFS), overall survival (OS), and safety and tolerability. Results: MOUNTAINEER enrolled 117 patients between 08Aug2017 and 22Sept2021. Data cutoff was 28Mar2022. The median age was 56.0 years (range, 24, 77), and baseline characteristics were balanced across cohorts. Eighty-six patients received at least 1 dose of study treatment in Cohorts A+B, and 30 patients received tucatinib monotherapy in Cohort C (total, 116). The overall median duration of follow-up was 16.3 months (IQR, 10.8, 28.2). In Cohorts A+B, the confirmed ORR by BICR was 38.1% (95% CI, 27.7, 49.3). The median DOR was 12.4 months (95% CI, 8.5, 20.5). The median PFS was 8.2 months (95% CI, 4.2, 10.3), and the median OS was 24.1 months (95% CI, 20.3, 36.7). The most common adverse events (AEs) in Cohorts A+B were diarrhoea (64.0%), fatigue (44.2%), nausea (34.9%), and infusion-related reaction (20.9%);the most common AE of grade ≥3 was hypertension (7.0%). Adverse events leading to tucatinib discontinuation in Cohorts A+B occurred in 5.8% of patients and included alanine amino transferase increase (2.3%), COVID-19 pneumonia (1.2%), cholangitis (1.2%), and fatigue (1.2%). No deaths resulted from AEs. Conclusions: In patients with chemotherapy-refractory HER2+ mCRC, tucatinib in combination with trastuzumab was well tolerated with clinically meaningful antitumor activity including durable responses and a median overall survival of 2 years. Tucatinib in combination with trastuzumab has the potential to become a new standard of care for patients with HER2+ mCRC. Clinical trial identification: NCT03043313. Editorial acknowledgement: The authors thank Joseph Giaconia of MMS Holdings, Michigan, USA for providing medical writing support/editorial support, which was funded by Seagen Inc., Bothell, WA, USA in accordance with Good Publication Practice (GPP3) guidelines. Legal entity responsible for the study: Seagen Inc. Funding: Seagen Inc. Disclosures: J. Strickler: Advisory / Consultancy: Seagen, Bayer, Pfizer;Research grant / Funding (institution): Amgen, Roche/Genentech, Seagen. A. Cercek: Advisory / Consultancy: Bayer, Merck, Seagen;Research grant / Funding (institution): Seagen, GSK, Rgenix. T. André: Honoraria (self : Amgen, Astra-Zeneca, Bristol-Myers Squibb, Gritstone Oncology, GlaxoSmithKline, Haliodx, Kaleido Biosciences, Merck & Co., Inc., Pierre Fabre, Sanofi, Servier, Merck & Co., Inc, Servier;Advisory / Consultancy: Astellas Pharma, BMS, Gritstone Oncology, Transgène, Roche/Ventana, Seagen, Merck & Co., Inc, Servier;Research grant / Funding (institution): BMS, Seagen, GSK;Travel / Accommodation / Expenses: BMS, Merck & Co., Inc. K. Ng: Advisory / Consultancy: Seattle Genetics, Bicara Therapeutics, GlaxoSmithKline;Research grant / Funding (institution): Pharmavite, Evergrande Group, Janssen. E. Van Cutsem: Advisory / Consultancy: AbbVie, Array, Astellas, AstraZeneca, Bayer, Beigene, Biocartis, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Daiichi, Halozyme, GSK, Helsinn, Incyte, Ipsen, Janssen Research, Lilly, Merck Sharp & Dohme, Merck KGaA, Mirati, Novartis, Pierre Fabre, Roche, Seattle Genetics, Servier, Sirtex, Terumo, Taiho, TRIGR, Zymeworks;Research grant / Funding (institution): Amgen, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Ipsen, Lilly, Merck Sharp & Dohme, Merck KGaA, Novartis, Roche, Servier. C. Wu: Research grant / Funding (institution): Seagen. A. Paulson: Research grant / Funding (institution): Seattle Genetics. J. Hubbard: Research grant / Funding (institution): Seattle Genetics. H. Lenz: Honoraria (self): BMS, Bayer, Roche;Advisory / Consultancy: Bayer, Merck, Roche;Travel / Accommodation / Expenses: BMS, Bayer, Merck KG;Shareholder / Stockholder / Stock options: Fulgent. M. Stecher: Full / Part-time employment: SeaGen. W. Feng: Full / Part-time employment: Seagen. T. Bekaii-Saab: Honoraria (self): Royalties: Uptodate;Advisory / Consultancy: Consulting (to institution): Ipsen, Arcus, Pfizer, Seattle Genetics, Bayer, Genentech, Incyte, Eisai and Merck., Consulting (to self): Stemline, AbbVie, Boehringer Ingelheim, Janssen, Daichii Sankyo, Natera, TreosBio, Celularity, Exact Science, Sobi, Beigene, Kanaph, Astra Zeneca, Deciphera, MJH Life Sciences, Aptitude Health, Illumina and Foundation Medicine, IDMC/DSMB: Fibrogen, Suzhou Kintor, Astra Zeneca, Exelixis, Merck/Eisai, PanCan and 1Globe;Research grant / Funding (institution): Agios, Arys, Arcus, Atreca, Boston Biomedical, Bayer, Eisai, Celgene, Lilly, Ipsen, Clovis, Seattle Genetics, Genentech, Novartis, Mirati, Merus, Abgenomics, Incyte, Pfizer, BMS.;Licensing / Royalties: WO/2018/183488: HUMAN PD1 PEPTIDE VACCINES AND USES THEREOF – Licensed to Imugene, WO/2019/055687: METHODS AND COMPOSITIONS FOR THE TREATMENT OF CANCER CACHEXIA – Licensed to Recursion. All other authors have declared no conflicts of interest.

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

Spatial Analysis of the Sociodemographic Characteristics, Comorbidities, Hospitalization, Signs, and Symptoms Among Hospitalized Coronavirus Disease 2019 Cases in the State of Rio De Janeiro, Brazil.

After more than 1 year from the beginning of the pandemic, the coronavirus disease 2019 (COVID-19) has reached all continents. The number of infected people is still increasing, and Brazil is among the countries with the highest number of registered cases in the world. In this study, we investigated the profile of hospitalized COVID-19 cases and the eventual clusters of similar areas, using geographic information systems. The study was conducted using secondary data. Variables such as sociodemographic characteristics, comorbidities, hospitalization, signs, and symptoms among confirmed cases were considered for each microregion/city of the state of Rio de Janeiro. These proportions were used when calculating the Global Moran's I. The local indicator of spatial association was used to identify local clusters. A significant global spatial auto correlation was found in 28% of the variables. The presence of spatial autocorrelation indicates that the proportions of patients with COVID-19 according to these characteristics are spatially oriented. Moran maps reveal 2 clusters, 1 of high proportions and 1 of low proportions. Understanding the geographic patterns of COVID-19 may assist public health investigators, contributing to actions to prevent and control the pandemic in the state.

Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches.

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the usefulness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases, up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending our methods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time.

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.

An immune-based biomarker signature is associated with mortality in COVID-19 patients.

Immune and inflammatory responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contribute to disease severity of coronavirus disease 2019 (COVID-19). However, the utility of specific immune-based biomarkers to predict clinical outcome remains elusive. Here, we analyzed levels of 66 soluble biomarkers in 175 Italian patients with COVID-19 ranging from mild/moderate to critical severity and assessed type I IFN-, type II IFN-, and NF-κB-dependent whole-blood transcriptional signatures. A broad inflammatory signature was observed, implicating activation of various immune and nonhematopoietic cell subsets. Discordance between IFN-α2a protein and IFNA2 transcript levels in blood suggests that type I IFNs during COVID-19 may be primarily produced by tissue-resident cells. Multivariable analysis of patients' first samples revealed 12 biomarkers (CCL2, IL-15, soluble ST2 [sST2], NGAL, sTNFRSF1A, ferritin, IL-6, S100A9, MMP-9, IL-2, sVEGFR1, IL-10) that when increased were independently associated with mortality. Multivariate analyses of longitudinal biomarker trajectories identified 8 of the aforementioned biomarkers (IL-15, IL-2, NGAL, CCL2, MMP-9, sTNFRSF1A, sST2, IL-10) and 2 additional biomarkers (lactoferrin, CXCL9) that were substantially associated with mortality when increased, while IL-1α was associated with mortality when decreased. Among these, sST2, sTNFRSF1A, IL-10, and IL-15 were consistently higher throughout the hospitalization in patients who died versus those who recovered, suggesting that these biomarkers may provide an early warning of eventual disease outcome.

Estimating the Cumulative Incidence of COVID-19 in the United States Using Four Complementary Approaches.

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the prevalence of COVID-19 across the United States (US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 in each state in the US as well as Puerto Rico and the District of Columbia, using a combination of excess influenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive test counts across the different states. Nationally, our estimates of COVID-19 symptomatic cases as of April 4 have a likely range of 2.2 to 4.9 million, with possibly as many as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 10.3 million, as opposed to 1.5 million positive test counts. The proposed combination of approaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.

Managing oncology clinical trials during COVID-19 pandemic.

São Paulo city is the epicenter of the Brazilian COVID-19 pandemic. The Instituto do Cancer do Estado de São Paulo is currently conducting 161 multinational sponsored trials plus 116 in house studies in the oncologic population. There are 242 currently active participants and 180 patients in follow-up. The management of the tightly controlled environment of clinical research becomes a challenge, and the Food and Drug Administration set of priority recommendations for patient safety while maintaining study integrity. Fast adaptations are necessary, and actions coalesce to participant protection from COVID-19. We pointed out critical processes for adjustments, and we believe that our experience may help other academic health centers.

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time (preprint)

Non-pharmaceutical interventions (NPIs) have been crucial in curbing COVID-19 in the United States (US). Consequently, relaxing NPIs through a phased re-opening of the US amid still-high levels of COVID-19 susceptibility could lead to new epidemic waves. This calls for a COVID-19 early warning system. Here we evaluate multiple digital data streams as early warning indicators of increasing or decreasing state-level US COVID-19 activity between January and June 2020. We estimate the timing of sharp changes in each data stream using a simple Bayesian model that calculates in near real-time the probability of exponential growth or decay. Analysis of COVID-19-related activity on social network microblogs, Internet searches, point-of-care medical software, and a metapopulation mechanistic model, as well as fever anomalies captured by smart thermometer networks, shows exponential growth roughly 2-3 weeks prior to comparable growth in confirmed COVID-19 cases and 3-4 weeks prior to comparable growth in COVID-19 deaths across the US over the last 6 months. We further observe exponential decay in confirmed cases and deaths 5-6 weeks after implementation of NPIs, as measured by anonymized and aggregated human mobility data from mobile phones. Finally, we propose a combined indicator for exponential growth in multiple data streams that may aid in developing an early warning system for future COVID-19 outbreaks. These efforts represent an initial exploratory framework, and both continued study of the predictive power of digital indicators as well as further development of the statistical approach are needed.

Estimating the Prevalence of COVID-19 in the United States: Three Complementary Approaches (preprint)

Effectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.