Background: Screening for viral infection in cancer patients is inconsistent. A mechanism to readily identify cancer patients at increased risk of existing or prior viral infection could enhance screening efforts while reducing costs. Methods: We identified factors associated with increased risk of past or chronic HBV, HCV, or HIV infection prior to initiation of systemic cancer therapy. Data were from a multicenter prospective cohort study of 3,051 patients with newly diagnosed cancer (SWOG-S1204) enrolled between 2013-2017. Patients completed a survey with questions pertaining to personal history, behavioral, socioeconomic, and demographic risk factors for viral hepatitis or HIV. We derived a risk model to predict the presence of viral infection in a random set of 60% of participants using best subset selection. The derived model was validated in the remaining 40% of participants. Logistic regression was used. Results: A model with 7 risk factors was identified, and a risk score with 4 levels was constructed. In the validation cohort, each increase in risk level was associated with nearly a threefold increased risk of viral positivity (OR = 2.85, 95%-CI, 2.26-3.60, p<0.001). Consistent findings were observed for individual viruses. Participants in the highest risk group (with ≥3 risk factors), comprised of 13.4% of participants, were 18 times more likely to be viral positive compared to participants with no risk factors (OR = 18.18, 95%-CI, 8.00-41.3, p<0.001). Conclusions and Relevance: A risk-stratified screening approach using a limited set of questions could serve as an effective strategy to streamline screening for individuals at increased risk of viral infection.

We consider the design of phase II trials evaluating combinations of experimental therapies. In the modern era, many immunotherapy and targeted therapy regimens are being developed as combination regimens, including combinations consisting only of experimental agents. In some clinical/drug development scenarios, it may be difficult to isolate the effect of the individual agents making up a combination of this type, which makes the evaluation of the combination challenging. One such scenario arises when none of the agents making up the experimental combination have demonstrated single-agent activity in the clinical setting of interest. One solution to this problem is to employ a randomized comparative trial in which the combination of interest is compared with one or both of its component agents, but because all arms in such a trial would be experimental, some modifications to more traditional randomized comparative phase II trials must be made. In this manuscript, we present sensible modifications to randomized phase II trial designs that can be employed in two common drug development scenarios of this type and provide a detailed discussion of the practical aspects of designing these trials. We also include two worked examples to further illustrate how to design such a trial.

Pathologists worldwide are facing remarkable challenges with the increasing workloads and the lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole slide images has the potential of democratizing the access to expert pathology and affordable biomarkers, by supporting pathologists in the provision of timely and accurate diagnosis as well as supporting oncologists by extracting prognostic and predictive biomarkers directly from tissue slides. The long-awaited adoption of AI in pathology, however, has not materialized, and the transformation of pathology is happening at a pace that is much slower than that observed in other fields (eg,, radiology). Here, we provide a critical summary of the developments in digital and computational pathology in the last ten years, outline key hurdles and ways to overcome them, and provide a perspective for AI-supported precision oncology in the future.

Background: The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from twelve cancer genome-wide association studies (GWAS) to quantify pair-wise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. Methods: We collected GWAS summary statistics for twelve solid cancers based on 376,759 cancer cases and 532,864 controls of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies (TWAS) to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. Results: We observed wide-spread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and TWAS, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least two cancer types by testing for pleiotropy at known cancer susceptibility loci. Conclusions: Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer- and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.

R Donald Harvey, PharmD; The Earlier the Better? Or Better Late than Never? Dose Optimization in Oncology, JNCI: Journal of the National Cancer Institute, , dja

Ya-Chen Tina Shih, PhD, Cathy Bradley, K Robin Yabroff; Ecological and Individualistic Fallacies in Health Disparities Research, JNCI: Journal of the National C

Background: We examined adverse birth outcomes among adolescent and young adult women diagnosed with cancer (AYA women, ages 15-39 years) during pregnancy. Methods: We linked data from the Texas Cancer Registry, vital records, and Texas Birth Defects Registry to identify all singleton births to AYA women diagnosed during pregnancy from January 1999 to December 2016. We compared prevalence of adverse live birth outcomes between AYA women and women without cancer (matched 1:4 on age, race and ethnicity, and year). Among AYA women, we used log-binomial regression to identify factors associated with these outcomes. Statistical tests were 2-sided. Results: AYA women had 1,271 singleton live births and 20 stillbirths. AYA women (n=1,291) were 33.3% Hispanic and 9.8% non-Hispanic Black and most commonly had breast (22.5%), thyroid (19.8%), and gynecologic (13.3%) cancers. Among live births, AYA women had a higher prevalence of low birth weight offspring (30.1% vs. 9.0%), very preterm (5.7% vs. 1.2%) and preterm birth (25.1% vs. 7.2%), cesarean delivery (44.3% vs. 35.2%), and low Apgar score (2.7% vs. 1.5%), compared to women without cancer (n=5,084) (all p<0.05). Prevalence of any birth defect by age 12 months did not statistically differ (5.2% vs. 4.7%, p=0.48), but live births to AYA women more often had heart and circulatory system defects (2.2% vs. 1.3%, p=0.01). In adjusted models, cancer type and chemotherapy were associated with adverse live birth outcomes. Conclusions: AYA women diagnosed during pregnancy have higher prevalence of adverse birth outcomes and face difficult decisions in balancing treatment risks and benefits.

This is a correction to: Holly J Pederson, MD, Stephanie S Faubion, MD, MBA, Sandhya Pruthi, MD, Shari Goldfarb, MD, RE: Systemic or vaginal hormone therapy after early breast cancer: a Danish observational cohort study, JNCI: Journal of the National Cancer Institute, Volume 115, Issue 2, February 2023, Pages 220–221, https://doi.org/10.1093/jnci/djac211

Background: We aimed to investigate whether the risk of second primary malignancy (SPM) in TC patients receiving RAI therapy increases in a cumulative dose-dependent manner compared to those without. Methods: Using the Korean National Health Insurance Service-National Health Information Database (NHIS-NHID, 2002–2019), we investigated hazard ratios (HRs) of SPM associated with RAI in TC. SPM was defined as a second primary malignancy diagnosed at least 1 year after TC diagnosis. Results: Of 217,777 TC patients (177,385 women and 40,392 men; mean age, 47.2 ± 11.6 years), 100,448 (46.1%) received RAI therapy. The median follow-up duration was 7.7 years (interquartile range [IQR]: 5.5–10.3) and the median cumulative RAI dose was 3.7 GBq (IQR 1.9–5.6). During 2004–2019, SPM incidence rates were 7.30 and 6.56 per 1,000 person-years in the RAI and non-RAI groups, respectively, with an unadjusted HR of 1.09 (95% CI, 1.05–1.13); this remained at 1.08 (95% CI, 1.04–1.13) after adjustment for multiple clinical confounding factors. Notably, SPM risk increased significantly from 3.7 GBq with full adjustments, and a strong linear association between cumulative RAI dose and SPM was observed in the restricted cubic spline analysis. Regarding cancer subtypes, myeloid leukemia and salivary gland, trachea, lung and bronchus, uterus, and prostate cancers had the most significantly elevated risks in patients who underwent RAI therapy. Conclusions: This study identified that SPM risk increased linearly in a dose-dependent manner in TC patients with RAI therapy compared to those without.

Although patient navigation has shown promise for increasing participation in colorectal cancer (CRC) screening and follow-up, little evidence is available to guide implementation of patient navigation in clinical practice. We characterize eight patient navigation programs being implemented as part of multi-component interventions of the National Cancer Institute's Cancer Moonshot^SM Accelerating Colorectal Cancer Screening and Follow-Up Through Implementation Science (ACCSIS) initiative. We developed a data collection template organized by ACCSIS framework domains. The template was populated by a representative from each of the eight ACCSIS research projects. We report standardized descriptions of 1) the socio-ecological context in which the navigation program was being conducted; 2) navigation program characteristics; 3) activities undertaken to facilitate program implementation (eg, training); and 4) outcomes used in program evaluation. ACCSIS patient navigation programs varied broadly in their socio-ecological context and settings, the populations they served, and in how they were implemented in practice. Six research projects adapted and implemented evidence-based patient navigation programs; the remaining projects developed new programs. Five projects began navigation when patients were due for initial CRC screening; three projects began navigation later in the screening process, when patients were due for follow-up colonoscopy after an abnormal stool-test result. Seven projects relied on existing clinical staff to deliver the navigation; one hired a centralized research navigator. All projects plan to evaluate effectiveness and implementation of their programs. Our detailed program descriptions may facilitate cross-project comparisons and guide future implementation and evaluation of patient navigation programs in clinical practice. Oregon: NCT04890054 North Carolina: NCT044067 San Diego: NCT04941300 Appalachia: NCT04427527 Chicago: NCT0451434 Oklahoma: Not registered Arizona: Not registered New Mexico: Not registered