Impact of the COVID-19 pandemic on stage at diagnosis of patients with breast cancer: An analysis of 11,752 patients from Oncoclínicas

Background: As a reaction to the COVID-19 pandemic, a nation-wide lockdown was enforced in Brazil in March 2020, cancer care was impacted, and cancer screening reduced. Therefore, an increase in cancer diagnoses at more advanced stages was expected. In this study, we extracted data from our nationwide real-world database to evaluate the impact of the COVID-19 pandemic on the stage at diagnosis of breast cancer (BC) cases. Methods: We explored curated electronic medical record data of female patients, over 18 years of age, diagnosed with BC and with established disease stage based on the AJCC 8th edition, who started treatment or follow-up in the Oncoclínicas (OC) between Jan 1, 2018, and Dec 31, 2021. The primary objective was to compare stage distribution at first visit during COVID- 19 pandemic (2020-2021) with a historical control cohort from a period prior to the pandemic (2018- 2019). We investigated stage distribution according to age at diagnosis and tumor ER/HER2 subtype in univariate models. Associations were considered significant if they had a minimum significance (P < 0.1 in Chi-square test). The historical numbers of patients with BC at OC make it possible to identify differences in the prevalence of stages in the order of 5% comparing pre and post pandemic periods with a statistical power greater than 80%. Results: We collected data for 11,752 patients with initial diagnosis of BC, with 6,492 patients belonging to the pandemic (2020-2021) and 5,260 patients to the pre-pandemic period (2018-2019). For both ER+/ HER2- and HER2+ tumors, there was a lower percentage of patients with early-stage (defined as stage I-II) in the years 2020-2021 vs 2018-2019 and a considerable increase in advanced-stage disease (defined as stage IV). For triple negative BC (TNBC), there was a significant higher percentage of patients with advanced-stage disease in the pandemic vs pre-pandemic period (table 1). Age over 50 years was associated with a greater risk of advanced stage at diagnosis after the onset of the pandemic, with an absolute increase of 7% (P twosided <0.01). Conclusions: We observed a substantial increase in cases of advanced-stage BC in OC institutions as a result of delays in BC diagnoses due to the COVID-19 pandemic. The impact appeared greater in older adults, potentially because of stricter confinement in this group.

Cancer control in Latin America and the Caribbean: recent advances and opportunities to move forward

The increasing burden of cancer represents a substantial problem for Latin America and the Caribbean. Two Lancet Oncology Commissions in 2013 and 2015 highlighted potential interventions that could advance cancer care in the region by overcoming existing challenges. Areas requiring improvement included insufficient investment in cancer control, non-universal health coverage, fragmented health systems, inequitable concentration of cancer services, inadequate registries, delays in diagnosis or treatment initiation, and insufficient palliative services. Progress has been made in key areas but remains uneven across the region. An unforeseen challenge, the COVID-19 pandemic, strained all resources, and its negative effect on cancer control is expected to continue for years. In this Series paper, we summarise progress in several aspects of cancer control since 2015, and identify persistent barriers requiring commitment of additional resources to reduce the cancer burden in Latin America and the Caribbean.

Early mortality linked to COVID-19 in cancer patients as compared to historical control in pre-pandemic times

Background: The COVID-19 pandemic remains a public health emergency of global concern, with higher mortality rates in cancer patients as compared to the general population. However, early mortality of COVID19 in cancer patients has not been compared to historical real-world data from oncology population in pre-pandemic times. Methods: Longitudinal multicenter cohort study of patients with cancer and confirmed COVID-19 from Oncoclínicas Group in Brazil from March to December 2020. The primary endpoint was 30-day mortality after isolation of the SARS-CoV-2 by RT-PCR. As historical control, we selected patients from Oncoclínicas Data Lake treated before December 2019 and propensity score-matched to COVID-19 cases (3:1) based on the following clinical characteristics: age, gender, tumor type, disease setting (curative or palliative), time from diagnosis of cancer (or metastatic disease) to COVID-19 infection. Results: In total, 533 cancer patients with COVID-19 were prospectively registered in the database, with median age 60 years, 67% females, most frequent tumor types breast (34%), hematological (16%), gastrointestinal (15%), genitourinary (12%) and respiratory tract malignancies (10%). Most patients were on active systemic therapy or radiotherapy (84%), largely for advanced or metastatic disease (55%). In the overall population, early death rate was 15%, which was numerically higher than the Brazilian general population with COVID-19 diagnosis in 2020 (2.5%). We were able to match 442 cancer patients with COVID-19 to 1,187 controls with cancer from pre-pandemic times. The 30-day mortality rate was 12.4% in COVID-19 cases as compared to 5.4% in pre-pandemic controls with cancer (Odds Ratio 2.49, 95%CI 1.67 - 3.70;P value < 0.01, Power 97.5%). COVID-19 cancer patients had significantly higher death events than historical controls (Hazard Ratio 2.18, 95%CI 1.52 - 3.12;P value < 0.01, Power 99.7%), particularly from 20 to 30 days after diagnosis of the infection. Conclusions: Cancer patients with COVID-19 have an excess mortality 30 days after the infection when compared to matched cancer population from pre-pandemic times and the general population with COVID-19, reinforcing the need for priority vaccination in public health strategies. Legal entity responsible for the study: Oncoclínicas Group. Funding: Amgen. Disclosure: All authors have declared no conflicts of interest.

Machine learning prediction of COVID-19 mortality in cancer patients

Background: COVID-19 is a challenge for clinical decision-making in cancer patients and the allocation of healthcare resources. An accurate prognosis prediction to effectively triage patients is needed, especially in the community oncology practice. Methods:Nationwide cohort from Oncoclínicas Brazil was used to validate previously developed multivariable logistic regression (mLR) model (Ferrari et al, JCO GO 2021) and to construct a machine learning Random Forest (RF) algorithm as predictor of 30-day mortality after SARS-CoV-2 detection by RT-PCR in cancer patients diagnosed in an outpatient setting. To find the most important baseline clinical determinants of early COVID19-related death via Gini index, a RF with 100,000 trees was trained in 75% of the dataset, and the performance was assessed in the remaining 25%. We then compared the accuracy of different models in terms of sensitivity, specificity and area under the receiver operating characteristics curves (AUC). Results:From March to December 2020, 533 patients with COVID-19 were prospectively registered in the database. Median age was 60 years (19-93) and 67% were female. Most frequent cancers were breast in 34%, hematological in 16%, and gastrointestinal in 15%. Comorbidities were common (52%), as was current/former smoking history (17%). Most patients were on active systemic therapy or radiotherapy (84%) in the advanced or metastatic disease setting (55%). The overall mortality rate was 15% (CI95% 12%-18%). We validated the original mLR model trained in the first 198 patients: management in a noncurative setting (odds ratio [OR] 3.7), age ≥ 60 years (OR 2.3), and current/former smoking (OR 1.9) were significant predictors of death in the expanded cohort. Presence of comorbidities (OR 1.9) also defined poor outcome in the updated mLR model, which yielded low sensitivity (74%), specificity (68%) and AUC (0.78). With RF modeling, the most significant predictors of 30-day death after COVID-19 (in decreasing order) were older age, treatment of advanced or metastatic disease, tumor type (respiratory tract, brain and unknown primary cancers had higher mortality), COVID-related symptom burden at baseline evaluation and treatment regimen (immunotherapy combinations had higher mortality). The RF model demonstrated high sensitivity (89%), specificity (88%) and AUC (0.96). Conclusions:The results highlight the possibility that machine learning algorithms are able to predict early mortality after COVID-19 in cancer patients with high accuracy. The proposed prediction model may be helpful in the prompt identification of high-risk patients based on clinical features alone, without having to wait for the results of additional tests such as laboratory or radiologic studies. It can also help prioritize medical resources and redefine vaccination strategies. A web-based mortality risk calculator will be created for clinical decision support.

IMpassion130: Final OS analysis from the pivotal phase III study of atezolizumab + nab-paclitaxel vs placebo + nab-paclitaxel in previously untreated locally advanced or metastatic triple-negative breast cancer

Background: Based on findings from IMpassion130, international guidelines now recommend atezolizumab (A) + nab-paclitaxel (nP) for patients (pts) with locally advanced or metastatic TNBC (mTNBC) whose tumours express PD-L1 on tumour-infiltrating immune cells (IC). Here we report prespecified final OS and long-term safety results. Methods: The study design and final PFS analysis have been reported (Schmid NEJM 2018). Pts were randomised 1:1 to A + nP or placebo (P) + nP. Co-primary endpoints were PFS (tested in parallel in ITT and PD-L1+ pts) and OS (tested hierarchically in ITT and, if significant, in PD-L1+ pts). Results: As of 14 April 2020, 666/902 pts (73.8%) had died;median OS follow-up was 18.8 mo (IQR, 8.9-34.7 mo). 6% of pts in the A + nP arm and 2% in the P + nP arm remained on any treatment. OS data are in the Table. 460 A + nP arm pts and 430 P + nP arm pts were safety evaluable, of whom 8% and 3%, respectively, received nP for up to 24 mo. Similarly, 5% in the A + nP arm received nP for ≥ 24 mo (vs 1% in the P + nP arm). Respectively, 51% vs 43% had a G 3-4 AE;≈ 1% per arm had a G 5 AE (no new G 5 AEs since last analysis;no patterns seen);24% vs 19% had a serious AE, and 59% vs 42% had an AE of special interest (G 3-4 in 8% vs 5%). No confirmed or suspected COVID-19 AEs were reported. 19% in the A + nP arm and 8% in the P + nP arm had an AE leading to treatment discontinuation (most commonly due to neuropathy);in 18% and 8%, respectively, AEs led to nP discontinuation, and in 8% and 1%, AEs led to A or P discontinuation. Conclusions: While OS differences for A + nP vs P + nP in the IMpassion130 ITT population were not statistically significant, precluding formal testing, clinically meaningful OS benefit was observed in PD-L1+ pts (7.5-mo median OS improvement). A + nP remained safe and tolerable with longer follow-up. Results from this final and mature OS analysis are consistent with prior interim analyses. [Formula presented] Clinical trial identification: NCT02425891. Editorial acknowledgement: Medical writing assistance for this abstract was provided by Ashley J. Pratt, PhD, of Health Interactions, and funded by F. Hoffmann-La Roche, Ltd. Legal entity responsible for the study: F. Hoffmann-La Roche, Ltd. Funding: F. Hoffmann-La Roche, Ltd. Disclosure: L.A. Emens: Honoraria (self): AbbVie, Amgen, Celgene, Chugai, Gritstone, MedImmune, Peregrine, Shionogi, Syndax;Honoraria (self), Travel/Accommodation/Expenses: AstraZeneca, Bayer, MacroGenics, Replimune, Vaccinex;Travel/Accommodation/Expenses: Bristol Myers Squibb, Genentech/Roche, Novartis;Research grant/Funding (institution): Aduro Biotech, AstraZeneca, The Breast Cancer Research Foundation, Bristol Myers Squibb, Bolt Therapeutics, Corvus, The US Department of Defense, EMD Serono, Genentech, Maxcyte, Merck, The National Cancer Institute, The NSABP Foundation, Roche, The Transl;Licensing/Royalties: Aduro;Advisory/Consultancy: Roche. S. Adams: Research grant/Funding (institution): Genentech;Research grant/Funding (institution): Merck, Amgen, BMS, Novartis, Celgene, Daiichi Sankyo. C.H. Barrios: Advisory/Consultancy: Boehringer- Ingelheim;Advisory/Consultancy: GSK;Advisory/Consultancy, Research grant/Funding (institution): Novartis;Advisory/Consultancy, Research grant/Funding (institution): Pfizer;Advisory/ Consultancy, Research grant/Funding (institution): Roche/Genentech;Advisory/Consultancy: Eisai;Advisory/Consultancy, Research grant/Funding (institution): Merck;Advisory/Consultancy, Research grant/Funding (institution): AstraZeneca;Non-remunerated activity/ies: Bayer;Research grant/ Funding (institution): AbbVie;Research grant/Funding (institution): Amgen;Research grant/Funding (institution): Astellas;Research grant/Funding (institution): BMS;Research grant/Funding (institution): Celgene;Research grant/Funding (institution): Lilly;Research grant/Funding (institution): Medivation;Research grant/Funding (institution): Sanofi;Research grant/Funding (institution): Taiho Pharmaceutical;Research grant/Funding (institution) Mylan;Research grant/Funding (institution): Merrimack;Research grant/Funding (institution): Biomarin;Research grant/Funding (institution): Daiichi Sankyo;Research grant/Funding (institution): Abraxis BioScience;Research grant/Funding (institution): AB Science;Research grant/Funding (institution): Asana BioSciences;Research grant/Funding (institution): Exelixis, Research grant/Funding (institution): ImClone Systems, Research grant/Funding (institution): LEO Pharma;Research grant/Funding (institution): Millennium;Advisory/Consultancy: Merck Sharp and Dohme;Advisory/Consultancy: AstraZeneca. V.C. Dieras: Honoraria (self), Advisory/Consultancy: Roche/Genentech, Pfizer, Lilly, Novartis, Daiichi Sankyo, AstraZeneca, AbbVie, Seattle Genetics, Odonate, MSD. H. Iwata: Honoraria (self), Advisory/Consultancy: Chugai;Honoraria (self), Advisory/Consultancy: Novartis, AstraZeneca, Pfizer, Lilly, Daiichi-Sankyo, Eisai, Kyowa Kirin;Non-remunerated activity/ies: MSD, Bayer, BI, Nihon, Kayaku, Sanofi. S. Loi: Research grant/Funding (institution), Non-remunerated activity/ies: Novartis, BMS, Roche-Genentech, Merck;Research grant/Funding (institution): Puma, Eli Lilly, Pfizer;Unpaid consultant: Seattle Genetics;Unpaid consultant: Pfizer;Unpaid consultant: Novartis;Unpaid consultant: BMS;Unpaid consultant: AstraZeneca;Unpaid consultant: Roche/Genentech;Advisory/Consultancy (institution): Aduro Biotechnology. H.S. Rugo: Research grant/Funding (institution): Pfizer, Novartis, Lilly, Genentech/Roche, Merck, OBI, Eisai, Plexxikon, Immunomedics;Research grant/Funding (institution), Travel/Accommodation/Expenses: Macrogeneics, Daiichi;Travel/Accommodation/Expenses: Puma, Mylan, Genentech/Roche, Novartis, Pfizer;Honoraria (self): Celltrion. A. Schneeweiss: Research grant/Funding (institution): Celgene, Roche, AbbVie, Molecular Partner;Advisory/Consultancy: Roche AstraZeneca;Travel/Accommodation/Expenses: Celgene, Roche, Pfizer;Honoraria (self): Roche, Celgene, Pfizer, AstraZeneca, Novartis, MSD, Tesaro, Lilly. E.P. Winer: Honoraria (self): Lilly, Genentech, Infinite MD, Carrick Therapeutics, GSK, Jounce, Genomic HEalth, Merck, Seattle Genetics;Honoraria (self), Leadership role: Leap. S. Patel: Shareholder/Stockholder/Stock options, Full/Part-time employment: F. Hoffmann-La Roche. V. Henschel: Shareholder/Stockholder/Stock options, Full/Part-time employment: F. Hoffmann-La Roche. A. Swat: Shareholder/Stockholder/Stock options, Full/Part-time employment: F. Hoffmann-La Roche. M. Kaul: Shareholder/Stockholder/Stock options, Full/Part-time employment: F. Hoffmann-La Roche. L. Molinero: Shareholder/Stockholder/Stock options, Full/Part-time employment: F. Hoffmann-La Roche. S.Y. Chui: Shareholder/Stockholder/Stock options, Full/Part-time employment: Genentech/Roche. P. Schmid: Honoraria (self), Advisory/Consultancy, Research grant/Funding (institution), Spouse/Financial dependant, spouse – consulting for Genentech: Roche;Honoraria (self): Medscape;Honoraria (self), Advisory/Consultancy: AstraZeneca;Honoraria (self): GI Therapeutics;Honoraria (self): Health Interactions;Advisory/Consultancy: Pfizer;Advisory/Consultancy, Research grant/Funding (institution): Novartis;Advisory/Consultancy: Merck;Advisory/Consultancy: Boehringer Ingelheim;Advisory/Consultancy: Bayer;Advisory/Consultancy: EISAI;Advisory/Consultancy: Celegence;Advisory/Consultancy: Puma;Research grant/Funding (institution), Spouse/Financial dependant, spouse – consulting for Genentech: Genentech;Research grant/Funding (institution): Oncogenex.