Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients.

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.

Breast cancer progression when definitive surgery is delayed.

Deferment of definitive surgery for some breast cancers has been proposed as a way to conserve hospital resources during the COVID-19 pandemic. However, it is currently unknown which, if any, breast cancers are capable of progressing during a few to several months of observation. The difference between clinical size at diagnosis and final pathology size was assessed in 315 stage I-III primary invasive breast cancer patients who were divided into three groups based on the time between diagnosis and definitive surgery. Size differences over time were used to estimate specific growth rates. Compared with the group with ≤60 days between diagnosis and surgery, tumor growth was observed for 12% of tumors in the 61- to 120-day group and 17% of tumors in the >120-day group (p for trend = 0.032). Significantly greater specific growth rates were observed for tumors >2 cm by pathology measurement and for pathology node-positive patients (p < 0.0001 and p = 0.006, respectively). Specific growth rates were significantly greater for luminal B breast cancers than for luminal A breast cancers (p = 0.029) but not for triple-negative or HER2-positive breast cancers not selected for neo-adjuvant chemotherapy. There was no evidence of nodal progression with surgery delay. Fewer than 20% of stage I-III breast cancers not selected for neo-adjuvant chemotherapy evidence size progression during follow-up periods ranging from 61 to 294 days. Clinical-pathological features cannot reliably predict which tumors will grow. Luminal B phenotype was the only clinical variable known at the time of diagnosis that strongly predicted growth. If resource limitations mandate prioritization schemes for breast cancer surgery, luminal B breast cancer may be the highest priority.

Recommendations for prioritization, treatment, and triage of breast cancer patients during the COVID-19 pandemic. the COVID-19 pandemic breast cancer consortium.

The COVID-19 pandemic presents clinicians a unique set of challenges in managing breast cancer (BC) patients. As hospital resources and staff become more limited during the COVID-19 pandemic, it becomes critically important to define which BC patients require more urgent care and which patients can wait for treatment until the pandemic is over. In this Special Communication, we use expert opinion of representatives from multiple cancer care organizations to categorize BC patients into priority levels (A, B, C) for urgency of care across all specialties. Additionally, we provide treatment recommendations for each of these patient scenarios. Priority A patients have conditions that are immediately life threatening or symptomatic requiring urgent treatment. Priority B patients have conditions that do not require immediate treatment but should start treatment before the pandemic is over. Priority C patients have conditions that can be safely deferred until the pandemic is over. The implementation of these recommendations for patient triage, which are based on the highest level available evidence, must be adapted to current availability of hospital resources and severity of the COVID-19 pandemic in each region of the country. Additionally, the risk of disease progression and worse outcomes for patients need to be weighed against the risk of patient and staff exposure to SARS CoV-2 (virus associated with the COVID-19 pandemic). Physicians should use these recommendations to prioritize care for their BC patients and adapt treatment recommendations to the local context at their hospital.